{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# import geopandas\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 想要探究各省的用水情况，读入2013-2018国家用水情况数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sgnyea</th>\n",
       "      <th>Prvcd</th>\n",
       "      <th>Prvnm</th>\n",
       "      <th>Twsply</th>\n",
       "      <th>Sfcws</th>\n",
       "      <th>Gndws</th>\n",
       "      <th>Othws</th>\n",
       "      <th>Twtus</th>\n",
       "      <th>Agcwu</th>\n",
       "      <th>Idswu</th>\n",
       "      <th>Cnspwu</th>\n",
       "      <th>Ecpwu</th>\n",
       "      <th>Pcwu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>4952.70</td>\n",
       "      <td>976.40</td>\n",
       "      <td>86.40</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>3693.10</td>\n",
       "      <td>1261.60</td>\n",
       "      <td>859.90</td>\n",
       "      <td>200.90</td>\n",
       "      <td>431.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>39.30</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.30</td>\n",
       "      <td>10.80</td>\n",
       "      <td>39.30</td>\n",
       "      <td>4.20</td>\n",
       "      <td>3.30</td>\n",
       "      <td>18.40</td>\n",
       "      <td>13.40</td>\n",
       "      <td>181.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>28.40</td>\n",
       "      <td>19.50</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>28.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>5.40</td>\n",
       "      <td>7.40</td>\n",
       "      <td>5.60</td>\n",
       "      <td>182.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>130000</td>\n",
       "      <td>河北省</td>\n",
       "      <td>182.40</td>\n",
       "      <td>70.40</td>\n",
       "      <td>106.10</td>\n",
       "      <td>5.80</td>\n",
       "      <td>182.40</td>\n",
       "      <td>121.10</td>\n",
       "      <td>19.10</td>\n",
       "      <td>27.80</td>\n",
       "      <td>14.50</td>\n",
       "      <td>241.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>140000</td>\n",
       "      <td>山西省</td>\n",
       "      <td>74.30</td>\n",
       "      <td>39.80</td>\n",
       "      <td>30.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>74.30</td>\n",
       "      <td>43.30</td>\n",
       "      <td>14.00</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.50</td>\n",
       "      <td>200.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>2003</td>\n",
       "      <td>640000</td>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>64.00</td>\n",
       "      <td>57.60</td>\n",
       "      <td>6.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.00</td>\n",
       "      <td>58.40</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1111.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>2003</td>\n",
       "      <td>650000</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>500.67</td>\n",
       "      <td>446.97</td>\n",
       "      <td>53.06</td>\n",
       "      <td>0.64</td>\n",
       "      <td>500.67</td>\n",
       "      <td>454.93</td>\n",
       "      <td>8.28</td>\n",
       "      <td>13.10</td>\n",
       "      <td>24.36</td>\n",
       "      <td>2608.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>2002</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>5497.28</td>\n",
       "      <td>4404.36</td>\n",
       "      <td>1072.42</td>\n",
       "      <td>20.49</td>\n",
       "      <td>5497.28</td>\n",
       "      <td>3736.18</td>\n",
       "      <td>1142.36</td>\n",
       "      <td>618.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>429.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>2001</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>5567.43</td>\n",
       "      <td>4450.65</td>\n",
       "      <td>1094.93</td>\n",
       "      <td>21.85</td>\n",
       "      <td>5567.43</td>\n",
       "      <td>3825.73</td>\n",
       "      <td>1141.81</td>\n",
       "      <td>599.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>437.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>2000</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>5530.73</td>\n",
       "      <td>4440.42</td>\n",
       "      <td>1069.17</td>\n",
       "      <td>21.14</td>\n",
       "      <td>5497.59</td>\n",
       "      <td>3783.54</td>\n",
       "      <td>1139.13</td>\n",
       "      <td>574.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>435.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>515 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sgnyea   Prvcd     Prvnm   Twsply    Sfcws    Gndws  Othws    Twtus  \\\n",
       "0      2018     142        中国  6015.50  4952.70   976.40  86.40  6015.50   \n",
       "1      2018  110000       北京市    39.30    12.30    16.30  10.80    39.30   \n",
       "2      2018  120000       天津市    28.40    19.50     4.40   4.60    28.40   \n",
       "3      2018  130000       河北省   182.40    70.40   106.10   5.80   182.40   \n",
       "4      2018  140000       山西省    74.30    39.80    30.00   4.50    74.30   \n",
       "..      ...     ...       ...      ...      ...      ...    ...      ...   \n",
       "510    2003  640000   宁夏回族自治区    64.00    57.60     6.50    NaN    64.00   \n",
       "511    2003  650000  新疆维吾尔自治区   500.67   446.97    53.06   0.64   500.67   \n",
       "512    2002     142        中国  5497.28  4404.36  1072.42  20.49  5497.28   \n",
       "513    2001     142        中国  5567.43  4450.65  1094.93  21.85  5567.43   \n",
       "514    2000     142        中国  5530.73  4440.42  1069.17  21.14  5497.59   \n",
       "\n",
       "       Agcwu    Idswu  Cnspwu   Ecpwu     Pcwu  \n",
       "0    3693.10  1261.60  859.90  200.90   431.92  \n",
       "1       4.20     3.30   18.40   13.40   181.75  \n",
       "2      10.00     5.40    7.40    5.60   182.23  \n",
       "3     121.10    19.10   27.80   14.50   241.98  \n",
       "4      43.30    14.00   13.40    3.50   200.27  \n",
       "..       ...      ...     ...     ...      ...  \n",
       "510    58.40     3.50    1.70    0.40  1111.50  \n",
       "511   454.93     8.28   13.10   24.36  2608.34  \n",
       "512  3736.18  1142.36  618.73     NaN   429.34  \n",
       "513  3825.73  1141.81  599.89     NaN   437.74  \n",
       "514  3783.54  1139.13  574.92     NaN   435.40  \n",
       "\n",
       "[515 rows x 13 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv (\"RES_Wsau.csv\", encoding = \"gbk\",)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 更改名字，方便整理数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>地区代码</th>\n",
       "      <th>地区</th>\n",
       "      <th>供水总量</th>\n",
       "      <th>地表水供水量</th>\n",
       "      <th>地下水供水量</th>\n",
       "      <th>其他供水量</th>\n",
       "      <th>用水总量</th>\n",
       "      <th>农业用水量</th>\n",
       "      <th>工业用水量</th>\n",
       "      <th>生活用水量</th>\n",
       "      <th>生态用水量</th>\n",
       "      <th>人均用水量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>4952.70</td>\n",
       "      <td>976.40</td>\n",
       "      <td>86.40</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>3693.10</td>\n",
       "      <td>1261.60</td>\n",
       "      <td>859.90</td>\n",
       "      <td>200.90</td>\n",
       "      <td>431.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>39.30</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.30</td>\n",
       "      <td>10.80</td>\n",
       "      <td>39.30</td>\n",
       "      <td>4.20</td>\n",
       "      <td>3.30</td>\n",
       "      <td>18.40</td>\n",
       "      <td>13.40</td>\n",
       "      <td>181.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>28.40</td>\n",
       "      <td>19.50</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>28.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>5.40</td>\n",
       "      <td>7.40</td>\n",
       "      <td>5.60</td>\n",
       "      <td>182.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>130000</td>\n",
       "      <td>河北省</td>\n",
       "      <td>182.40</td>\n",
       "      <td>70.40</td>\n",
       "      <td>106.10</td>\n",
       "      <td>5.80</td>\n",
       "      <td>182.40</td>\n",
       "      <td>121.10</td>\n",
       "      <td>19.10</td>\n",
       "      <td>27.80</td>\n",
       "      <td>14.50</td>\n",
       "      <td>241.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>140000</td>\n",
       "      <td>山西省</td>\n",
       "      <td>74.30</td>\n",
       "      <td>39.80</td>\n",
       "      <td>30.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>74.30</td>\n",
       "      <td>43.30</td>\n",
       "      <td>14.00</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.50</td>\n",
       "      <td>200.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>2003</td>\n",
       "      <td>640000</td>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>64.00</td>\n",
       "      <td>57.60</td>\n",
       "      <td>6.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.00</td>\n",
       "      <td>58.40</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1111.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>2003</td>\n",
       "      <td>650000</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>500.67</td>\n",
       "      <td>446.97</td>\n",
       "      <td>53.06</td>\n",
       "      <td>0.64</td>\n",
       "      <td>500.67</td>\n",
       "      <td>454.93</td>\n",
       "      <td>8.28</td>\n",
       "      <td>13.10</td>\n",
       "      <td>24.36</td>\n",
       "      <td>2608.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>2002</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>5497.28</td>\n",
       "      <td>4404.36</td>\n",
       "      <td>1072.42</td>\n",
       "      <td>20.49</td>\n",
       "      <td>5497.28</td>\n",
       "      <td>3736.18</td>\n",
       "      <td>1142.36</td>\n",
       "      <td>618.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>429.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>2001</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>5567.43</td>\n",
       "      <td>4450.65</td>\n",
       "      <td>1094.93</td>\n",
       "      <td>21.85</td>\n",
       "      <td>5567.43</td>\n",
       "      <td>3825.73</td>\n",
       "      <td>1141.81</td>\n",
       "      <td>599.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>437.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>2000</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>5530.73</td>\n",
       "      <td>4440.42</td>\n",
       "      <td>1069.17</td>\n",
       "      <td>21.14</td>\n",
       "      <td>5497.59</td>\n",
       "      <td>3783.54</td>\n",
       "      <td>1139.13</td>\n",
       "      <td>574.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>435.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>515 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       年份    地区代码        地区     供水总量   地表水供水量   地下水供水量  其他供水量     用水总量  \\\n",
       "0    2018     142        中国  6015.50  4952.70   976.40  86.40  6015.50   \n",
       "1    2018  110000       北京市    39.30    12.30    16.30  10.80    39.30   \n",
       "2    2018  120000       天津市    28.40    19.50     4.40   4.60    28.40   \n",
       "3    2018  130000       河北省   182.40    70.40   106.10   5.80   182.40   \n",
       "4    2018  140000       山西省    74.30    39.80    30.00   4.50    74.30   \n",
       "..    ...     ...       ...      ...      ...      ...    ...      ...   \n",
       "510  2003  640000   宁夏回族自治区    64.00    57.60     6.50    NaN    64.00   \n",
       "511  2003  650000  新疆维吾尔自治区   500.67   446.97    53.06   0.64   500.67   \n",
       "512  2002     142        中国  5497.28  4404.36  1072.42  20.49  5497.28   \n",
       "513  2001     142        中国  5567.43  4450.65  1094.93  21.85  5567.43   \n",
       "514  2000     142        中国  5530.73  4440.42  1069.17  21.14  5497.59   \n",
       "\n",
       "       农业用水量    工业用水量   生活用水量   生态用水量    人均用水量  \n",
       "0    3693.10  1261.60  859.90  200.90   431.92  \n",
       "1       4.20     3.30   18.40   13.40   181.75  \n",
       "2      10.00     5.40    7.40    5.60   182.23  \n",
       "3     121.10    19.10   27.80   14.50   241.98  \n",
       "4      43.30    14.00   13.40    3.50   200.27  \n",
       "..       ...      ...     ...     ...      ...  \n",
       "510    58.40     3.50    1.70    0.40  1111.50  \n",
       "511   454.93     8.28   13.10   24.36  2608.34  \n",
       "512  3736.18  1142.36  618.73     NaN   429.34  \n",
       "513  3825.73  1141.81  599.89     NaN   437.74  \n",
       "514  3783.54  1139.13  574.92     NaN   435.40  \n",
       "\n",
       "[515 rows x 13 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.rename(columns = {\"Sgnyea\":\"年份\", \"Prvcd\":\"地区代码\", \"Prvnm\":\"地区\", \"Twsply\":\"供水总量\", \"Sfcws\":\"地表水供水量\", \"Gndws\":\"地下水供水量\", \"Othws\":\"其他供水量\", \"Twtus\":\"用水总量\",\"Agcwu\":\"农业用水量\",\"Idswu\":\"工业用水量\",\"Cnspwu\":\"生活用水量\",\"Ecpwu\":\"生态用水量\",\"Pcwu\":\"人均用水量\"})\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看数据类型，更改不可计算数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 515 entries, 0 to 514\n",
      "Data columns (total 13 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   年份      515 non-null    int64  \n",
      " 1   地区代码    515 non-null    int64  \n",
      " 2   地区      515 non-null    object \n",
      " 3   供水总量    515 non-null    float64\n",
      " 4   地表水供水量  515 non-null    float64\n",
      " 5   地下水供水量  512 non-null    float64\n",
      " 6   其他供水量   412 non-null    float64\n",
      " 7   用水总量    515 non-null    float64\n",
      " 8   农业用水量   515 non-null    float64\n",
      " 9   工业用水量   515 non-null    float64\n",
      " 10  生活用水量   515 non-null    float64\n",
      " 11  生态用水量   496 non-null    float64\n",
      " 12  人均用水量   514 non-null    float64\n",
      "dtypes: float64(10), int64(2), object(1)\n",
      "memory usage: 52.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2[\"年份\"] = df2[\"年份\"].astype(\"str\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 根据上图分析，发现13-18年间，水资源变化明显，像查看这两个年的水资源变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>地区代码</th>\n",
       "      <th>地区</th>\n",
       "      <th>供水总量</th>\n",
       "      <th>地表水供水量</th>\n",
       "      <th>地下水供水量</th>\n",
       "      <th>其他供水量</th>\n",
       "      <th>用水总量</th>\n",
       "      <th>农业用水量</th>\n",
       "      <th>工业用水量</th>\n",
       "      <th>生活用水量</th>\n",
       "      <th>生态用水量</th>\n",
       "      <th>人均用水量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>4952.70</td>\n",
       "      <td>976.40</td>\n",
       "      <td>86.40</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>3693.10</td>\n",
       "      <td>1261.60</td>\n",
       "      <td>859.90</td>\n",
       "      <td>200.90</td>\n",
       "      <td>431.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>39.30</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.30</td>\n",
       "      <td>10.80</td>\n",
       "      <td>39.30</td>\n",
       "      <td>4.20</td>\n",
       "      <td>3.30</td>\n",
       "      <td>18.40</td>\n",
       "      <td>13.40</td>\n",
       "      <td>181.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>28.40</td>\n",
       "      <td>19.50</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>28.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>5.40</td>\n",
       "      <td>7.40</td>\n",
       "      <td>5.60</td>\n",
       "      <td>182.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>130000</td>\n",
       "      <td>河北省</td>\n",
       "      <td>182.40</td>\n",
       "      <td>70.40</td>\n",
       "      <td>106.10</td>\n",
       "      <td>5.80</td>\n",
       "      <td>182.40</td>\n",
       "      <td>121.10</td>\n",
       "      <td>19.10</td>\n",
       "      <td>27.80</td>\n",
       "      <td>14.50</td>\n",
       "      <td>241.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>140000</td>\n",
       "      <td>山西省</td>\n",
       "      <td>74.30</td>\n",
       "      <td>39.80</td>\n",
       "      <td>30.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>74.30</td>\n",
       "      <td>43.30</td>\n",
       "      <td>14.00</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.50</td>\n",
       "      <td>200.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2013</td>\n",
       "      <td>610000</td>\n",
       "      <td>陕西省</td>\n",
       "      <td>89.21</td>\n",
       "      <td>54.64</td>\n",
       "      <td>33.50</td>\n",
       "      <td>1.07</td>\n",
       "      <td>89.21</td>\n",
       "      <td>58.06</td>\n",
       "      <td>13.76</td>\n",
       "      <td>15.12</td>\n",
       "      <td>2.26</td>\n",
       "      <td>237.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2013</td>\n",
       "      <td>620000</td>\n",
       "      <td>甘肃省</td>\n",
       "      <td>121.99</td>\n",
       "      <td>90.92</td>\n",
       "      <td>29.43</td>\n",
       "      <td>1.64</td>\n",
       "      <td>121.99</td>\n",
       "      <td>99.23</td>\n",
       "      <td>13.07</td>\n",
       "      <td>7.90</td>\n",
       "      <td>1.79</td>\n",
       "      <td>472.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2013</td>\n",
       "      <td>630000</td>\n",
       "      <td>青海省</td>\n",
       "      <td>28.20</td>\n",
       "      <td>24.32</td>\n",
       "      <td>3.78</td>\n",
       "      <td>0.11</td>\n",
       "      <td>28.20</td>\n",
       "      <td>22.77</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.22</td>\n",
       "      <td>489.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2013</td>\n",
       "      <td>640000</td>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>72.13</td>\n",
       "      <td>66.40</td>\n",
       "      <td>5.56</td>\n",
       "      <td>0.17</td>\n",
       "      <td>72.13</td>\n",
       "      <td>63.44</td>\n",
       "      <td>5.01</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1108.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2013</td>\n",
       "      <td>650000</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>588.04</td>\n",
       "      <td>472.19</td>\n",
       "      <td>114.68</td>\n",
       "      <td>1.17</td>\n",
       "      <td>588.04</td>\n",
       "      <td>557.69</td>\n",
       "      <td>12.78</td>\n",
       "      <td>11.74</td>\n",
       "      <td>5.83</td>\n",
       "      <td>2615.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       年份    地区代码        地区     供水总量   地表水供水量  地下水供水量  其他供水量     用水总量  \\\n",
       "0    2018     142        中国  6015.50  4952.70  976.40  86.40  6015.50   \n",
       "1    2018  110000       北京市    39.30    12.30   16.30  10.80    39.30   \n",
       "2    2018  120000       天津市    28.40    19.50    4.40   4.60    28.40   \n",
       "3    2018  130000       河北省   182.40    70.40  106.10   5.80   182.40   \n",
       "4    2018  140000       山西省    74.30    39.80   30.00   4.50    74.30   \n",
       "..    ...     ...       ...      ...      ...     ...    ...      ...   \n",
       "187  2013  610000       陕西省    89.21    54.64   33.50   1.07    89.21   \n",
       "188  2013  620000       甘肃省   121.99    90.92   29.43   1.64   121.99   \n",
       "189  2013  630000       青海省    28.20    24.32    3.78   0.11    28.20   \n",
       "190  2013  640000   宁夏回族自治区    72.13    66.40    5.56   0.17    72.13   \n",
       "191  2013  650000  新疆维吾尔自治区   588.04   472.19  114.68   1.17   588.04   \n",
       "\n",
       "       农业用水量    工业用水量   生活用水量   生态用水量    人均用水量  \n",
       "0    3693.10  1261.60  859.90  200.90   431.92  \n",
       "1       4.20     3.30   18.40   13.40   181.75  \n",
       "2      10.00     5.40    7.40    5.60   182.23  \n",
       "3     121.10    19.10   27.80   14.50   241.98  \n",
       "4      43.30    14.00   13.40    3.50   200.27  \n",
       "..       ...      ...     ...     ...      ...  \n",
       "187    58.06    13.76   15.12    2.26   237.35  \n",
       "188    99.23    13.07    7.90    1.79   472.87  \n",
       "189    22.77     2.93    2.27    0.22   489.97  \n",
       "190    63.44     5.01    1.64    2.05  1108.63  \n",
       "191   557.69    12.78   11.74    5.83  2615.36  \n",
       "\n",
       "[192 rows x 13 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "变化_2018 = df2[df2['年份'].isin(['2013','2014','2015','2016','2017','2018'])]#isin()筛选某列等于多个数值或者字符串\n",
    "变化_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>地区代码</th>\n",
       "      <th>地区</th>\n",
       "      <th>供水总量</th>\n",
       "      <th>地表水供水量</th>\n",
       "      <th>地下水供水量</th>\n",
       "      <th>其他供水量</th>\n",
       "      <th>用水总量</th>\n",
       "      <th>农业用水量</th>\n",
       "      <th>工业用水量</th>\n",
       "      <th>生活用水量</th>\n",
       "      <th>生态用水量</th>\n",
       "      <th>人均用水量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>4952.70</td>\n",
       "      <td>976.40</td>\n",
       "      <td>86.40</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>3693.10</td>\n",
       "      <td>1261.6</td>\n",
       "      <td>859.90</td>\n",
       "      <td>200.90</td>\n",
       "      <td>431.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2017</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>6043.40</td>\n",
       "      <td>4945.50</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>81.20</td>\n",
       "      <td>6043.40</td>\n",
       "      <td>3766.40</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>838.10</td>\n",
       "      <td>161.90</td>\n",
       "      <td>435.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2016</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>6040.16</td>\n",
       "      <td>4912.40</td>\n",
       "      <td>1057.00</td>\n",
       "      <td>70.85</td>\n",
       "      <td>6040.20</td>\n",
       "      <td>3768.00</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>821.60</td>\n",
       "      <td>142.60</td>\n",
       "      <td>438.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2015</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>6103.20</td>\n",
       "      <td>4971.50</td>\n",
       "      <td>1069.20</td>\n",
       "      <td>62.50</td>\n",
       "      <td>6103.20</td>\n",
       "      <td>3851.50</td>\n",
       "      <td>1334.8</td>\n",
       "      <td>794.20</td>\n",
       "      <td>122.70</td>\n",
       "      <td>445.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2014</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>6094.88</td>\n",
       "      <td>4920.46</td>\n",
       "      <td>1116.94</td>\n",
       "      <td>57.46</td>\n",
       "      <td>6094.86</td>\n",
       "      <td>3868.98</td>\n",
       "      <td>1356.1</td>\n",
       "      <td>766.58</td>\n",
       "      <td>103.20</td>\n",
       "      <td>446.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2013</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>6183.45</td>\n",
       "      <td>5007.29</td>\n",
       "      <td>1126.22</td>\n",
       "      <td>49.94</td>\n",
       "      <td>6183.45</td>\n",
       "      <td>3921.52</td>\n",
       "      <td>1406.4</td>\n",
       "      <td>750.10</td>\n",
       "      <td>105.38</td>\n",
       "      <td>455.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       年份  地区代码  地区     供水总量   地表水供水量   地下水供水量  其他供水量     用水总量    农业用水量  \\\n",
       "0    2018   142  中国  6015.50  4952.70   976.40  86.40  6015.50  3693.10   \n",
       "32   2017   142  中国  6043.40  4945.50  1016.70  81.20  6043.40  3766.40   \n",
       "64   2016   142  中国  6040.16  4912.40  1057.00  70.85  6040.20  3768.00   \n",
       "96   2015   142  中国  6103.20  4971.50  1069.20  62.50  6103.20  3851.50   \n",
       "128  2014   142  中国  6094.88  4920.46  1116.94  57.46  6094.86  3868.98   \n",
       "160  2013   142  中国  6183.45  5007.29  1126.22  49.94  6183.45  3921.52   \n",
       "\n",
       "      工业用水量   生活用水量   生态用水量   人均用水量  \n",
       "0    1261.6  859.90  200.90  431.92  \n",
       "32   1277.0  838.10  161.90  435.91  \n",
       "64   1308.0  821.60  142.60  438.12  \n",
       "96   1334.8  794.20  122.70  445.09  \n",
       "128  1356.1  766.58  103.20  446.75  \n",
       "160  1406.4  750.10  105.38  455.54  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "总变化_2018 = 变化_2018[变化_2018['地区'].str.contains('中国') ]\n",
    "总变化_2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 中国的总数据没有用，删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>地区代码</th>\n",
       "      <th>地区</th>\n",
       "      <th>供水总量</th>\n",
       "      <th>地表水供水量</th>\n",
       "      <th>地下水供水量</th>\n",
       "      <th>其他供水量</th>\n",
       "      <th>用水总量</th>\n",
       "      <th>农业用水量</th>\n",
       "      <th>工业用水量</th>\n",
       "      <th>生活用水量</th>\n",
       "      <th>生态用水量</th>\n",
       "      <th>人均用水量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>39.3</td>\n",
       "      <td>12.3</td>\n",
       "      <td>16.3</td>\n",
       "      <td>10.8</td>\n",
       "      <td>39.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>18.4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>181.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>28.4</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>28.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>182.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>130000</td>\n",
       "      <td>河北省</td>\n",
       "      <td>182.4</td>\n",
       "      <td>70.4</td>\n",
       "      <td>106.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>182.4</td>\n",
       "      <td>121.1</td>\n",
       "      <td>19.1</td>\n",
       "      <td>27.8</td>\n",
       "      <td>14.5</td>\n",
       "      <td>241.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>140000</td>\n",
       "      <td>山西省</td>\n",
       "      <td>74.3</td>\n",
       "      <td>39.8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>74.3</td>\n",
       "      <td>43.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>200.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>150000</td>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>192.1</td>\n",
       "      <td>99.5</td>\n",
       "      <td>88.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>192.1</td>\n",
       "      <td>140.3</td>\n",
       "      <td>15.9</td>\n",
       "      <td>11.2</td>\n",
       "      <td>24.6</td>\n",
       "      <td>758.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2017</td>\n",
       "      <td>610000</td>\n",
       "      <td>陕西省</td>\n",
       "      <td>93.0</td>\n",
       "      <td>58.2</td>\n",
       "      <td>32.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>93.0</td>\n",
       "      <td>58.2</td>\n",
       "      <td>14.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>243.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2017</td>\n",
       "      <td>620000</td>\n",
       "      <td>甘肃省</td>\n",
       "      <td>116.1</td>\n",
       "      <td>87.1</td>\n",
       "      <td>25.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>116.1</td>\n",
       "      <td>92.3</td>\n",
       "      <td>10.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>443.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2017</td>\n",
       "      <td>630000</td>\n",
       "      <td>青海省</td>\n",
       "      <td>25.8</td>\n",
       "      <td>20.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>25.8</td>\n",
       "      <td>19.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>433.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2017</td>\n",
       "      <td>640000</td>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>66.1</td>\n",
       "      <td>60.3</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>66.1</td>\n",
       "      <td>56.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>974.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2017</td>\n",
       "      <td>650000</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>552.3</td>\n",
       "      <td>440.9</td>\n",
       "      <td>109.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>552.3</td>\n",
       "      <td>514.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>14.7</td>\n",
       "      <td>10.2</td>\n",
       "      <td>2280.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      年份    地区代码        地区   供水总量  地表水供水量  地下水供水量  其他供水量   用水总量  农业用水量  工业用水量  \\\n",
       "1   2018  110000       北京市   39.3    12.3    16.3   10.8   39.3    4.2    3.3   \n",
       "2   2018  120000       天津市   28.4    19.5     4.4    4.6   28.4   10.0    5.4   \n",
       "3   2018  130000       河北省  182.4    70.4   106.1    5.8  182.4  121.1   19.1   \n",
       "4   2018  140000       山西省   74.3    39.8    30.0    4.5   74.3   43.3   14.0   \n",
       "5   2018  150000    内蒙古自治区  192.1    99.5    88.7    3.9  192.1  140.3   15.9   \n",
       "..   ...     ...       ...    ...     ...     ...    ...    ...    ...    ...   \n",
       "59  2017  610000       陕西省   93.0    58.2    32.6    2.3   93.0   58.2   14.3   \n",
       "60  2017  620000       甘肃省  116.1    87.1    25.1    3.9  116.1   92.3   10.4   \n",
       "61  2017  630000       青海省   25.8    20.7     5.0    0.2   25.8   19.2    2.5   \n",
       "62  2017  640000   宁夏回族自治区   66.1    60.3     5.5    0.2   66.1   56.7    4.5   \n",
       "63  2017  650000  新疆维吾尔自治区  552.3   440.9   109.8    1.6  552.3  514.4   13.1   \n",
       "\n",
       "    生活用水量  生态用水量    人均用水量  \n",
       "1    18.4   13.4   181.75  \n",
       "2     7.4    5.6   182.23  \n",
       "3    27.8   14.5   241.98  \n",
       "4    13.4    3.5   200.27  \n",
       "5    11.2   24.6   758.84  \n",
       "..    ...    ...      ...  \n",
       "59   17.0    3.5   243.21  \n",
       "60    8.7    4.7   443.47  \n",
       "61    2.9    1.2   433.08  \n",
       "62    2.3    2.5   974.28  \n",
       "63   14.7   10.2  2280.78  \n",
       "\n",
       "[62 rows x 13 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#反选\"~\"，将[地区]中包含“中国”的删除\n",
    "#变化_2018 = 变化_2018[ ~ 变化_2018['地区'].str.contains('中国') ]\n",
    "#变化_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>供水总量</th>\n",
       "      <th>地表水供水量</th>\n",
       "      <th>地下水供水量</th>\n",
       "      <th>其他供水量</th>\n",
       "      <th>用水总量</th>\n",
       "      <th>农业用水量</th>\n",
       "      <th>工业用水量</th>\n",
       "      <th>生态用水量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <th>年份</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">中国</th>\n",
       "      <th>2013</th>\n",
       "      <td>6183.45</td>\n",
       "      <td>5007.29</td>\n",
       "      <td>1126.22</td>\n",
       "      <td>49.94</td>\n",
       "      <td>6183.45</td>\n",
       "      <td>3921.52</td>\n",
       "      <td>1406.4</td>\n",
       "      <td>105.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>6094.88</td>\n",
       "      <td>4920.46</td>\n",
       "      <td>1116.94</td>\n",
       "      <td>57.46</td>\n",
       "      <td>6094.86</td>\n",
       "      <td>3868.98</td>\n",
       "      <td>1356.1</td>\n",
       "      <td>103.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>6103.20</td>\n",
       "      <td>4971.50</td>\n",
       "      <td>1069.20</td>\n",
       "      <td>62.50</td>\n",
       "      <td>6103.20</td>\n",
       "      <td>3851.50</td>\n",
       "      <td>1334.8</td>\n",
       "      <td>122.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>6040.16</td>\n",
       "      <td>4912.40</td>\n",
       "      <td>1057.00</td>\n",
       "      <td>70.85</td>\n",
       "      <td>6040.20</td>\n",
       "      <td>3768.00</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>142.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>6043.40</td>\n",
       "      <td>4945.50</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>81.20</td>\n",
       "      <td>6043.40</td>\n",
       "      <td>3766.40</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>161.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>6015.50</td>\n",
       "      <td>4952.70</td>\n",
       "      <td>976.40</td>\n",
       "      <td>86.40</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>3693.10</td>\n",
       "      <td>1261.6</td>\n",
       "      <td>200.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            供水总量   地表水供水量   地下水供水量  其他供水量     用水总量    农业用水量   工业用水量   生态用水量\n",
       "地区 年份                                                                      \n",
       "中国 2013  6183.45  5007.29  1126.22  49.94  6183.45  3921.52  1406.4  105.38\n",
       "   2014  6094.88  4920.46  1116.94  57.46  6094.86  3868.98  1356.1  103.20\n",
       "   2015  6103.20  4971.50  1069.20  62.50  6103.20  3851.50  1334.8  122.70\n",
       "   2016  6040.16  4912.40  1057.00  70.85  6040.20  3768.00  1308.0  142.60\n",
       "   2017  6043.40  4945.50  1016.70  81.20  6043.40  3766.40  1277.0  161.90\n",
       "   2018  6015.50  4952.70   976.40  86.40  6015.50  3693.10  1261.6  200.90"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "中国水资源使用情况 = 总变化_2018.groupby ( by = ['地区','年份'] ) \\\n",
    "           .agg ({ \"供水总量\" : 'sum', \\\n",
    "                   \"地表水供水量\" : 'sum',\\\n",
    "                   \"地下水供水量\" : 'sum', \\\n",
    "                   \"其他供水量\" : 'sum',\\\n",
    "                   \"用水总量\" : 'sum',\\\n",
    "                   \"农业用水量\" : 'sum',\\\n",
    "                   \"工业用水量\" : 'sum',\\\n",
    "                   \"生态用水量\" : 'sum',\\\n",
    "                 }) \\\n",
    "           # .pivot(columns=\"年份\", values=\"水资源总量\" )\n",
    "中国水资源使用情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地区</th>\n",
       "      <th>年份</th>\n",
       "      <th>供水总量</th>\n",
       "      <th>地表水供水量</th>\n",
       "      <th>地下水供水量</th>\n",
       "      <th>其他供水量</th>\n",
       "      <th>用水总量</th>\n",
       "      <th>农业用水量</th>\n",
       "      <th>工业用水量</th>\n",
       "      <th>生态用水量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国</td>\n",
       "      <td>2013</td>\n",
       "      <td>6183.45</td>\n",
       "      <td>5007.29</td>\n",
       "      <td>1126.22</td>\n",
       "      <td>49.94</td>\n",
       "      <td>6183.45</td>\n",
       "      <td>3921.52</td>\n",
       "      <td>1406.4</td>\n",
       "      <td>105.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中国</td>\n",
       "      <td>2014</td>\n",
       "      <td>6094.88</td>\n",
       "      <td>4920.46</td>\n",
       "      <td>1116.94</td>\n",
       "      <td>57.46</td>\n",
       "      <td>6094.86</td>\n",
       "      <td>3868.98</td>\n",
       "      <td>1356.1</td>\n",
       "      <td>103.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中国</td>\n",
       "      <td>2015</td>\n",
       "      <td>6103.20</td>\n",
       "      <td>4971.50</td>\n",
       "      <td>1069.20</td>\n",
       "      <td>62.50</td>\n",
       "      <td>6103.20</td>\n",
       "      <td>3851.50</td>\n",
       "      <td>1334.8</td>\n",
       "      <td>122.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中国</td>\n",
       "      <td>2016</td>\n",
       "      <td>6040.16</td>\n",
       "      <td>4912.40</td>\n",
       "      <td>1057.00</td>\n",
       "      <td>70.85</td>\n",
       "      <td>6040.20</td>\n",
       "      <td>3768.00</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>142.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中国</td>\n",
       "      <td>2017</td>\n",
       "      <td>6043.40</td>\n",
       "      <td>4945.50</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>81.20</td>\n",
       "      <td>6043.40</td>\n",
       "      <td>3766.40</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>161.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>中国</td>\n",
       "      <td>2018</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>4952.70</td>\n",
       "      <td>976.40</td>\n",
       "      <td>86.40</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>3693.10</td>\n",
       "      <td>1261.6</td>\n",
       "      <td>200.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   地区    年份     供水总量   地表水供水量   地下水供水量  其他供水量     用水总量    农业用水量   工业用水量  \\\n",
       "0  中国  2013  6183.45  5007.29  1126.22  49.94  6183.45  3921.52  1406.4   \n",
       "1  中国  2014  6094.88  4920.46  1116.94  57.46  6094.86  3868.98  1356.1   \n",
       "2  中国  2015  6103.20  4971.50  1069.20  62.50  6103.20  3851.50  1334.8   \n",
       "3  中国  2016  6040.16  4912.40  1057.00  70.85  6040.20  3768.00  1308.0   \n",
       "4  中国  2017  6043.40  4945.50  1016.70  81.20  6043.40  3766.40  1277.0   \n",
       "5  中国  2018  6015.50  4952.70   976.40  86.40  6015.50  3693.10  1261.6   \n",
       "\n",
       "    生态用水量  \n",
       "0  105.38  \n",
       "1  103.20  \n",
       "2  122.70  \n",
       "3  142.60  \n",
       "4  161.90  \n",
       "5  200.90  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "中国水资源使用情况 = 中国水资源使用情况.reset_index()\n",
    "中国水资源使用情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>地表水供水量</th>\n",
       "      <th>地下水供水量</th>\n",
       "      <th>其他供水量</th>\n",
       "      <th>供水总量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>5007.29</td>\n",
       "      <td>1126.22</td>\n",
       "      <td>49.94</td>\n",
       "      <td>6183.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>4920.46</td>\n",
       "      <td>1116.94</td>\n",
       "      <td>57.46</td>\n",
       "      <td>6094.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>4971.50</td>\n",
       "      <td>1069.20</td>\n",
       "      <td>62.50</td>\n",
       "      <td>6103.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>4912.40</td>\n",
       "      <td>1057.00</td>\n",
       "      <td>70.85</td>\n",
       "      <td>6040.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>4945.50</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>81.20</td>\n",
       "      <td>6043.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>4952.70</td>\n",
       "      <td>976.40</td>\n",
       "      <td>86.40</td>\n",
       "      <td>6015.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份   地表水供水量   地下水供水量  其他供水量     供水总量\n",
       "0  2013  5007.29  1126.22  49.94  6183.45\n",
       "1  2014  4920.46  1116.94  57.46  6094.88\n",
       "2  2015  4971.50  1069.20  62.50  6103.20\n",
       "3  2016  4912.40  1057.00  70.85  6040.16\n",
       "4  2017  4945.50  1016.70  81.20  6043.40\n",
       "5  2018  4952.70   976.40  86.40  6015.50"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "总供水情况 = 总变化_2018.groupby(by = [\"年份\",\"地表水供水量\",\"地下水供水量\",'其他供水量'])\\\n",
    "                        .agg({\"供水总量\":\"mean\"})\\\n",
    "                        .reset_index()\n",
    "总供水情况#.数量.to_dict() #.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>地表水供水量</th>\n",
       "      <th>地下水供水量</th>\n",
       "      <th>其他供水量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>5007.29</td>\n",
       "      <td>1126.22</td>\n",
       "      <td>49.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>4920.46</td>\n",
       "      <td>1116.94</td>\n",
       "      <td>57.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>4971.50</td>\n",
       "      <td>1069.20</td>\n",
       "      <td>62.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>4912.40</td>\n",
       "      <td>1057.00</td>\n",
       "      <td>70.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>4945.50</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>81.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>4952.70</td>\n",
       "      <td>976.40</td>\n",
       "      <td>86.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份   地表水供水量   地下水供水量  其他供水量\n",
       "0  2013  5007.29  1126.22  49.94\n",
       "1  2014  4920.46  1116.94  57.46\n",
       "2  2015  4971.50  1069.20  62.50\n",
       "3  2016  4912.40  1057.00  70.85\n",
       "4  2017  4945.50  1016.70  81.20\n",
       "5  2018  4952.70   976.40  86.40"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "总供水情况1 = (总供水情况.drop(columns = ['供水总量']))\n",
    "总供水情况1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>年份</th>\n",
       "      <th>农业用水量</th>\n",
       "      <th>工业用水量</th>\n",
       "      <th>生态用水量</th>\n",
       "      <th>用水总量</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>3921.52</td>\n",
       "      <td>1406.4</td>\n",
       "      <td>105.38</td>\n",
       "      <td>6183.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>3868.98</td>\n",
       "      <td>1356.1</td>\n",
       "      <td>103.20</td>\n",
       "      <td>6094.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>3851.50</td>\n",
       "      <td>1334.8</td>\n",
       "      <td>122.70</td>\n",
       "      <td>6103.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>3768.00</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>142.60</td>\n",
       "      <td>6040.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>3766.40</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>161.90</td>\n",
       "      <td>6043.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>3693.10</td>\n",
       "      <td>1261.6</td>\n",
       "      <td>200.90</td>\n",
       "      <td>6015.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份    农业用水量   工业用水量   生态用水量     用水总量\n",
       "0  2013  3921.52  1406.4  105.38  6183.45\n",
       "1  2014  3868.98  1356.1  103.20  6094.86\n",
       "2  2015  3851.50  1334.8  122.70  6103.20\n",
       "3  2016  3768.00  1308.0  142.60  6040.20\n",
       "4  2017  3766.40  1277.0  161.90  6043.40\n",
       "5  2018  3693.10  1261.6  200.90  6015.50"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "农工生态水资源使用变化 = 总变化_2018.groupby(by = [\"年份\",\"农业用水量\",\"工业用水量\",'生态用水量'])\\\n",
    "                         .agg({\"用水总量\":\"mean\"})\\\n",
    "                         .reset_index()\n",
    "农工生态水资源使用变化#.数量.to_dict() #.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>3921.52</td>\n",
       "      <td>1406.4</td>\n",
       "      <td>105.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>3868.98</td>\n",
       "      <td>1356.1</td>\n",
       "      <td>103.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>3851.50</td>\n",
       "      <td>1334.8</td>\n",
       "      <td>122.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>3768.00</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>142.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>3766.40</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>161.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>3693.10</td>\n",
       "      <td>1261.6</td>\n",
       "      <td>200.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份    农业用水量   工业用水量   生态用水量\n",
       "0  2013  3921.52  1406.4  105.38\n",
       "1  2014  3868.98  1356.1  103.20\n",
       "2  2015  3851.50  1334.8  122.70\n",
       "3  2016  3768.00  1308.0  142.60\n",
       "4  2017  3766.40  1277.0  161.90\n",
       "5  2018  3693.10  1261.6  200.90"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "水资源使用变化1 = (农工生态水资源使用变化.drop(columns = ['用水总量']))\n",
    "水资源使用变化1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
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       "      <td>1334.8</td>\n",
       "      <td>122.70</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1308.0</td>\n",
       "      <td>142.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
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       "      <td>1277.0</td>\n",
       "      <td>161.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>3693.10</td>\n",
       "      <td>1261.6</td>\n",
       "      <td>200.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份    农业用水量   工业用水量   生态用水量\n",
       "0  2013  3921.52  1406.4  105.38\n",
       "1  2014  3868.98  1356.1  103.20\n",
       "2  2015  3851.50  1334.8  122.70\n",
       "3  2016  3768.00  1308.0  142.60\n",
       "4  2017  3766.40  1277.0  161.90\n",
       "5  2018  3693.10  1261.6  200.90"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "水资源使用变化1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>用水总量</th>\n",
       "      <th>供水总量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>6183.45</td>\n",
       "      <td>6183.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>6094.86</td>\n",
       "      <td>6094.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>6103.20</td>\n",
       "      <td>6103.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>6040.20</td>\n",
       "      <td>6040.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>6043.40</td>\n",
       "      <td>6043.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>6015.50</td>\n",
       "      <td>6015.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份     用水总量     供水总量\n",
       "0  2013  6183.45  6183.45\n",
       "1  2014  6094.86  6094.88\n",
       "2  2015  6103.20  6103.20\n",
       "3  2016  6040.20  6040.16\n",
       "4  2017  6043.40  6043.40\n",
       "5  2018  6015.50  6015.50"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "供水_用水变化 = 总变化_2018.groupby(by = [\"年份\"])\\\n",
    "                           .agg({\"用水总量\":\"mean\",\\\n",
    "                                 \"供水总量\":\"mean\"})\\\n",
    "                           .reset_index()\n",
    "供水_用水变化#.数量.to_dict() #.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "总供水情况1.plot(kind='bar', figsize=(10, 6))\n",
    "总供水情况1.plot(kind='line')\n",
    "plt.xlabel('年份') # add to x-label to the plot\n",
    "plt.ylabel('供水量变化') # add y-label to the plot\n",
    "plt.title('2013-2018中国供水的总体情况的变化') # add title to the plot\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 由图我们可以看出，2013-2018年间，供水情况并没有过大的变化，但是可以看出，关于地下水供水量呈现出微小的下降趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "水资源使用变化1.plot(kind='bar', figsize=(10, ))\n",
    "水资源使用变化1.plot(kind='line')\n",
    "plt.xlabel('年份') # add to x-label to the plot\n",
    "plt.ylabel('用水量变化') # add y-label to the plot\n",
    "plt.title('2013-2018中国用水的总体情况的变化') # add title to the plot\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 由图我们可以看出，2013-2018年间，国家对于农业用水，工业用水都在逐年缓慢下降的，而生态用水是在呈现上升状态。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17f07b24b70>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2)\n",
    "供水_用水变化.plot(kind='area', stacked=False, ax=ax1)\n",
    "供水_用水变化.plot(kind='area', ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 从上图可知，供水情况主要以地表水供给为主，我们来看看2013-2018年间地表水的供给情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "总供水情况1['地表水供水量'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('地表水供水量[2013 - 2018]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=总供水情况1.index, loc='upper left') \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SEZGkUUkTEenultoY4Azn+LEZOUHHkaQaBpwD/Jlw5BngJuBhisfXBBtLRES2hEqaiEh3tNRCeKszngHsBxo16+YMONi/fU04chve6NqHgaYSEZF2UUkTEelOlpoBxzjHBWbo3Nr0NAg4G/gj4cgCvNG1B3QdNhGRriOlS1prl8zvCAsXLtTy+yLStSy1g5zjEjPGatRM8EbXDvBvawlHbgeu1rlrIiKpTyt6iYh0dUutqO5dexZ42oyxQceRlDQA+D3wAeHIjYQjw4MOJCIiTVNJExHpqpbatrF37d/OEckIMTnoONIlZAGnAB8RjlxHODIs6EAiIvJtKmkiIl3NUiuIvWfXOMf7oRBHm6HJjdJW2cBvgI8JR64iHBkcdCAREdlMJU1EpKtYar3ce3Z+LManIeNUs9Q+r1i6hFzgdOATwpESwpGCoAOJiIhKmohI6ltqGSy139TF+MSMv4ZC9Aw6knQ7ecCZwKeEI38nHOkfdCARkXSW0n+F3XDBBeOSeby+55/f7GqRa9euzTjiiCO2raurIy8vL/bII498kpub65KZQUSkTZbaTjW13JmVyfgM/VlNOl5PYCbwW8KRq4BZFI+vCDiTiEja0Ud+gptuuqn/73//+1UvvfTSh4MGDaq5//77+wSdSUTS1FILbVxsM+vqeDMrk/FBx5G00xv4C/AO4cghQYcREUk3KT2S1tlmzpy5On5/7dq1mUOGDKkNMo+IpKfKN2z72jr+07snhUFnkbQ3CniCcOQe4AyKx38dcB4RkbSgkbRG/Pe//+25YcOGzAMPPHBT0FlEJI0sNSt9zc7OyuQdFTRJMccA7xGO/DzoICIi6UAlrYFVq1ZlnHHGGSPmzJnzWdBZRCR91LxlI8s2srBfXy7JzCQ76DwijegP/ItwZD7hyA5Bh2kNMxtsZllB5xARaSuVtATRaNSmT5++3YUXXvjVjjvuWB10HhFJD6UL7XSM9/v00rln0iUcALxJOHIO4UhSCpCZ9TWzJ8zsaTN70Myy/ecHm9mSFva9xcxeMbNz/cenmtnrZtYT+J5zriYZGUVEOpNKWoLZs2cPfOedd/L+/ve/D504ceJON910U7+gM4lI91X9lm1VFrGX+/XhqqxMcoLOI9IGucDfgEWEI3sm4XjHAVc45w4GVgLxxUpKgB5N7WRmPwAynHOTgG3NbAdgDHAzMAHQaQsi0iWl9MIhLS2Zn2wzZsxYPWPGjNUtbykismXWvmo/692Tf/Tp1fQvoCJdwO7AS4Qj1wMzKB7frlLknPtHwsMC4Gszm4xXslY2s+v+wL3+/aeBfQADsoCD8YqkiEiXo5E0EZFOdM/lZl+F7ZYB+dyanaWCJt1CCPgt8BrhyE5bciAzmwT0AxbjXQJgZgu79AS+8u+vAwbjlbXvA8uAeWZ2wJZkEhEJgkqaiEgnuesyG7LvOBZvNZgTg84i0gF2A14nHDmyPTubWX/gGuBEvHL2D+fc+hZ228jm6ZC9gJBzbi7wV2A98BjQrjwiIkFSSRMR6QQPXGOTvrcPb209hDFBZxHpQL2B/xCOlBCOtPqUCn+hkPuAPznnPge+C/zWzBYAY8zs5iZ2XYQ3xRGgEPjMv78D8DFQhX7XEZEuSD+4REQ62NO32M++tw/PDuzHwKCziHSSM4FnCUeGtHL7XwBFwDl+MbvOObe/c25/4A3n3ElmtquZNTzH7CHgJ2Z2BXAU8JiZ9cE7j+1d4BTgv1v+7YiIdC6VtAZWrVqV8eCDD/ZZsWJFSi+qIiKpb9pkC710t105eU9u6dlD559J2tkPWEw4sm9LGzrnrnfO9YsXM3/KYvy1/f2v7zrnzm2wXxne4iGvAgc45zY458qcc88458qdc2Occw8k85sSEekMKV1EZi2pGZfM480cm9XsapGrV6/OOOSQQ3Y4+OCD18+YMWPrBQsWfDBs2LDaZGYQkfTw+59Zj4vO4OHdd+SgoLOIBGgoMJ9wZAbF46/oiDdwzpWyeYVHEZFuIaVLWmdbuHBhj5KSki8PPPDATaWlpZmvvPJK3pFHHlkWdC4R6Vr+dZGNPOMEnh45jB2DziKSAjKBywlHJgEnUjy+POhAIiKpTtMdE0yZMmXjgQceuOmJJ57otXjx4p4HHHDAxqAziUjX8sA1tt/hB7JYBU3kW34ILCQc2TXoICIiqU4lrYFYLMbdd9/dv2/fvrXZ2dku6Dwi0nU8eoP9/NB9eXpAPv2DziKSonYGXiYcKQ46iIhIKlNJayAUCnHHHXd8MXr06Mp77rknP+g8IpL6pk220N0l9qeD9uLGHrnkBJ1HJMX1BZ4kHDki6CAiIqlKJS3BOeecM+Taa68dALB+/fqM/v371wWdSURS27TJ1uPwyVz+w+/xfznZZAWdR6SLyAXuIxw5OeggIiKpSCUtwRlnnLH67rvv7j9+/Pid6urq7IgjjtCiISLSpGmTLW/qAVxzwnROy87SQkwibZQB/JNw5NwWtxQRSTMp/UtFS0vmJ1tBQUHdyy+//GFnvqeIdE3TJlvPww/kup8ezvGZmWQEnUekC7uQcKQAOIPi8ToXXEQEjaSJiLTZtMnWKxTirCn7MUUFTSQpfgfcQDhiQQcREUkFKmkiIm0wbbL1Bs6Kxdj6/Gu5dd16VgWdSaSbOAW4lXBEv5uISNrTD0IRkVaaNtl6AWcDw4CvVqymYuYVzFm7npUBRxPpLn4K3Ek4ktKnY4iIdDSVNBGR1usBDAE2xZ9YuYbKmZdz+5pSVgQXS6RbOQaYSzii1VJFJG2ppImItNK8+W41cAmQh3etJwBWrfWK2up1LA8snEj38gO8ETWdoyYiaSmlpxPMLp09LpnHO73f6a1aLfLLL7/MPPjgg3d877333k3m+4tI1zdvvvto2mS7FG/aI8AGgK/XEZ1xOXfM+gPHDxrAVsElFOk2jgK+BM4KOoiISGfTSFojTjvttOHRaFR/vRORRs2b7z5m84hafvz5NaVeUVu1lmWBhRPpXs4kHDkt6BAiIp1NJa2BefPm9c7Ly6srKCioDTqLiKSuefPdJ8AsIBfoF39+7XqqZpRw56o1KmoiSXIV4cgRQYcQEelMKmkJotGoXXTRRUOvvvrqr4LOIiKpb9589yleUcsmoait20DV2SXcsXINXwYWTqT7CAF3EY5MCjqIiEhnUUlLcO655w755S9/uXrgwIF1QWcRka5h3nz3GV5RywL6x58vLaP6j5dx54rVfBFUNpFupAcwj3Bkh6CDiIh0BpW0BAsWLOhzww03DJo4ceJO7733Xo+jjz56ZNCZRCT1zZvvPscrahkkFLUN5V5RW/41nwcWTqT7GAg8QThSEHQQEZGOppKWIBKJvL9w4cL3Fy5c+P4uu+xSOXfuXP1iJSKtMm+++wL4O2DAgPjzZRup+eNl3PXVKj4NLJxI97Ed8CjhSF7QQUREOlJKL8Hf2iXzO8LChQvfD+q9RaRrmjffLZs22WYBM/H+6r8GoHwTNX+8jLsvPYtjth7CtoGGFOn6JgL3EI78gOLxOj1BRLoljaSJiCTJTYvH9ZlaUjQNb0QthlfUANhYQe1Zl3HPFyv4OLCAIt3HNODqoEOIiHQUlTQRkSS4afG4POAx4B9TS4pOxytqdSQUtYpKav94Kfd8vpyPAoop0p38hnDklKBDiIh0BJU0EZEtUFicbwefMHiUc+5hYB//6bOnlhSdibeYSC0JRa2yirqzL+Pfn33FhwHEFeluriIc2TXoECIiyaaSJiLSToXF+ZaVY4ftO33gi2b23QYvnzm1pOhsvKJWA9SvSOcXtbmfLkPnvopsmR5456flBB1ERCSZVNJERNopI9MO+P5JQ68dMip3qyY2OWNqSdGf8IpaFTAo/kK0mro/Xsa9n3zJ0s7IKtKN7QFcFnQIEZFkSunVHQlHxiX1eMXjm10tsqamhhEjRuw+fPjwaoBrr732i4kTJ1YmNYOIdAuFxfn7HHbikOu32r7HqBY2/d3UkqKMx//8xv/VVcdm4BW1rwGqa4idXcJ9l5zJD7cbwS4dnVmkGzuNcORpisc/GnQQEZFk0Ehagtdeey1v+vTp6+LXSlNBE5HGFBbnT9jzsP6Xbbt7zx1buctvD7t4zAUZ2aFZQAUwOP6CX9T+89HnvNshYUXSx62EI8OCDiEikgwqaQlefPHFns8880z+7rvvvstRRx01sqamJuhIIpJiCovz99i+sOcFRQfmT2zjrr867OIxF/lFrZyEqY81tcRmXM79H37GO0kNK5JeBgJ3EI7odxsR6fL0gyzBpEmTNj377LMfvPXWW+/V1NTYvffe2zfoTCKSOgqL80cMGJo9c/KPBxWHQtaen58nH3bxmFk5vTMvBTaSMKIWL2rvf8pbSQsskn4mA2cHHUJEZEuppCWYOHFi5ciRI2sAxo0bV/HBBx/kBp1JRFJDYXF+3+zc0JlTThry3ezcUN4WHOoXB5+/x2V+USsDhsRfqK3D/ekKHlz6CW9ucWCR9HUh4UhbR7pFRFKKSlqCI488cptXXnmlR21tLY8++mj+2LFjK4LOJCLBKyzOzwJ+OeWkIVP69M8qaHGHlv3s4PP3uDynT9alQCkNitrMK3jo3Y/5XxLeRyQdZeIty98n6CAiIu2lkpbgggsuWP7Tn/50m1133XXXCRMmbJo+fXp50JlEJFiFxfkG/Kj4yIFHb7Vdj+2SeOgTDj5v99k9C3JKgHXA0PgLsRjuz1fy8Dsf8UYS308knWwL/CPoECIi7ZXaS/C3sGR+sk2YMCH6wQcfaIU1EUm0z67f6f2L0Xv3GdsBxz5u8ozdQs9d+s6pG7+uOhMYBiyHzUXtojOIjd6Bog54b5Hu7jjCkdspHv900EFERNpKI2kiIk0oLM7ffsionDP2O3Lg3mZmHfQ2xxxw9m7X5w/Puxzv+mn1I2rOwTlX8cib79Opf7AS6UauJhzJCjqEiEhbqaSJiDSisDh/QF7vjD8e+vMhB2RmhXI6+O2O2vf0nf85YLteV+IVtfprPTkHf7maR99YyusdnEGkO9oJOD3oECIibZVqJS0Wi8U66q/VW8zPFgs6h4h0rMLi/ByMU79/8tBDevbJ7NdJb3vkXr/e8eaBO/S+ElgBbBV/wTk472oeX/IuCzspi0h3ch7hyNCWNxMRSR2pVtLeXr16dd9ULGqxWMxWr17dF3g76Cwi0nH8hUJO2HvqgMMGDc8Z0clvP33SL3e4dcjo/NnAMhKKGsD51/LEond4tZMziXR1vYFLgg4hItIWKbVwSG1t7UkrV668eeXKlaNJvQIZA96ura09KeggItKhDirYOufQPfbt2xELhbTGtAk/23bO4rs+/dlXS0pPBYbjFTYALriOp/7yG9yE0UwKKJ9IV3Q84cgNFI9/OeggIiKtkVIlbdy4cV8D04LOISLpqbA4fyfg2O/9ZNC4jEwLcrGB7xcdt80dlmHHL4us+y0wkoSiduE/ePrcXxGbuAd7BxdRpEsx4FrCkfEUj9dpCyKS8lJttEpEJBCFxfk9gFP2nT5gRP6g7M6e5tiYQ8f+eNTd2+xTcB3wGd6IWr2/3cB/X/0fLwaSTKRrGgucEnQIEZHWUEkTEfFMH7hV9vDRe/fdJ+ggCb43evrwf2+3/6DrgY+BrRNfvPhGnn15Cc8HE02kS7qIcKR/0CFERFqikiYiac+f5njI904YPCHgaY6NOWjX7289d6fvDb0Rr6h9Y0Rt1k0899JiwsFEE+ly+gN/CzqEiEhLVNJEJK350xxP2ufwAcP7DcoeFXSeJhy440FD79tlyrAbgQ+Ab0zHvORmFrwQYUEgyUS6nl8SjowJOoSISHNU0kQk3R0+cFj2yN336Zvqi3AcsP0BQx7cbdrWNwNLaVDULvsX4fDrzA8mmkiXEgIuDTqEiEhzVNJEJG0VFufviDfNcXxGpmUHnacV9tt2v0EPFf5oxC3AuzQoapffygvPvcazwUQT6VIOIhwZF3QIEZGmqKSJSFoqLM7PBU7aa+qA4f0GZ28TdJ422GfEdwbOG/PjkbcB7+Atz1/vyjm8+OyrPBNIMpGu5U9BBxARaYpKmoikq8MHDM0euce+KbWaY2vtNXz8gEfG/3SbOcCbNChqs2/n5Wde5rTZRM4AACAASURBVOlgool0GUcQjuwYdAgRkcaopIlI2ikszt8eOPS7xw4qzMzqEtMcG7Pn0N37PTbh59veCfwPGJX44jV38srTL/FUIMlEuoYQcHbQIUREGqOSJiJpxZ/mePJ2hT17FGyds1PQebbQxCG75T8+6dc73A0sxitqFn/x2rt49YkXeCKocCJdwE8IR7YKOoSISEMqaSKSbr4PDJo0ZcB3gg6SJOMHbtf7ib1+s+NcYBHe1Mf6onb9PSx8LMxjzgWWTySVZQN/CDqEiEhDKmkikjYKi/MHA4ftsW/fnPyCrJEt7tB1FA3YtteT+56x871AhAZF7ca5RB5dwKMqaiKNOoVwpF/QIUREEqmkiUg6ORyoLZqcf0DQQTrAmPyt857e7/c7/wdYSIOpjzfdx6JHnuMRFTWRb+kFnBp0CBGRRCppIpIWCovzRwCTJh7Sb0Cv/MwhQefpIHv03Srv6eI/7PIg8CoNitrN/2HxQ8/ycMyhqibyTb8jHMkLOoSISJxKmoh0e4XF+Qb8MCPTqnffp293HEVLtHufYT2emTxz14eAl2hQ1G59gDcefEZFTaSBgcBJQYcQEYlTSRORdLADULj3tAFb9+iZkQ7nnuzWc2Dus5P/tNtjwIs0KGpzHuJ/DzzNQ7GYippIgjMJR7KCDiEiAippItJFmNlQM/uumfVu5LXhTe1XWJwfAo7K6RGq3HlC7/06NGRq2aXngJxnD/zzbk8AL9CgqN3+MG/e9xQPqKiJ1BuBd96qiEjgVNJEpFOZ2WAze8G/P8LMFpjZfDP7p5lZE/vsCMwF9gbCZpZtZn83s6f8fZqbwjga2H6/HwzcPjs31CvZ30+K2ymvf86zB523+5NAGK+o1f/cv+sR3r73Se6PxYgFFVAkxfwk6AAiIqCSJiKdyMz6AXOAnv5TvwR+7ZybDAwHdm9i1z2AnzvnLgA+AbYBCvAu4DwW+KKxnQqL8zOAY3rlZ1RsV9hz76R9I13Ljrl9sp47+Pzd/ws8h7c8f/3P/rsf5Z17HldRE/EdSjgyIOgQIiIqaSLSmeqAo4EyAOfcOc659/zXBgBrGtvJOfcf4HMzmwL0Az7Cm7qXCeyHN0rUmPHA0OIfFhRmZoVyk/ZddD3b5/TOmn/wX3d/DniWBiNqcx/n3bsf5T91KmoiWcBRQYcQEVFJE5FO45wrc85taPi8mR0NvOOcW97M7r3wfnn6HHDA23ijQjHgeTPbJXHjwuL8bODHvftnbhyxc97EZH0PXdh2Ob2ynjvkb4XPA0/ToKjd+yTv3fUI96moiWjKo4gETyVNRAJlZtsCZwFnNLedc269c+6neH/pnuCcuxK4A6gAHgCmNNhlH6Dfnof23zkjwzKTn7xL2iYrN2PBoX8rfBl4igZF7T9PsfSOh5lbV0ddUAFFUsAkwpFtgw4hIulNJU1EAuOfo3YPcGJjI2wJ211vZvGVGfOB9Qn3y4EqEn6eFRbn5wFHZmTa16N2y5vQIeG7rpGZuRnPHXpR4avAE3hFLSP+4gPP8MGch1TUJO0dH3QAEUlvKmkiEqSZeMteX+Ov8lhsZpPN7NQG210KXOyvCrnQOfe+v+Lj/4CFwGl887y0iUDeuAPzt8npkdGnE76PrmZEZk7GgkMvKowAj+FNG60vag89y4e3Psi/a+uoDSyhSLCOCzqAiKQ3TQESkU7nnNvf/zoDmNHIJvMbbP8p3vTFxOc+SHhYfz6av6Lj94E1O03ofWCSIndHW2fmZDw3ZdaYyY/NfMPh/Zt9jre4C/Pm81Esxr9PPJIfZ2bos0LSzo6EIxMpHr8w6CAikp40kiYi3c2uwMDtx/Ts23dAVpMXuRYAtgplhhZMuWTM/4CHaTCi9ugCPr7pPu7WiJqkKS0gIiKBUUkTke7mUGBj4X75WtGxdYaGMkILplwy5l02F7X6kbMnnufTf87lrtpaagJLKBKMowlHNIosIoFQSRORbqOwOH9rYJfe/TI3DR6Rs1vQebqQIaGM0HNTLhm7FHiQBkXtyRf57Ia53FWjoibppQD4XtAhRCQ9qaSJSHdyAFA77rv5u4e07H5bDQ5l2HNTLhn7IXAfDYra0y/x+fX3cGdNLdWBJRTpfMcGHUBE0lObS5qZ5ZnZfDMb1optB5rZX9uVTESkDQqL83sC+wGrttmtZ1HQebqoAr+ofQbcS4Oi9t9X+OK6u7mzukZFTdLGdwlHLOgQIpJ+2jOSVgXsj3dB2Zb0Ac5ux3uIiLTVWCBzh7G9Bvfsmzk46DBd2MBQhs2fcsnYL4F/4xW1+p/381/ly2vv4o7qGqoCSyjSeQYBo4MOISLpp9mSZmZFZjbWzHYzs0Iz2xMwvKJWZ2YnmNmPzOwH/tdjzayXmS00s4FAFKjshO9DRNJYYXG+4Z07Ujp6rz4aRdtyA0IZ9uyUS8auwLvY+HASitqChSy7+k4VNUkbBwQdQETST0sjafcDEeBNYAnwIjAA2AQ44DZgLt75C3OB64FaYDxQgXe9nboOyC0ikmgksHWPXhnRIaNy9Vfv5OgfyrD/Trlk7NfAXTQoas+/zlezb+f2qmqigSUU6RyTgw4gIumnNdMdD8MbPdsfKPWfiyW8Xpjwujnn4h/Y+guriHSWvYGa3ffus0NGpmUHHaYb6RfKsGemXDK2FLgTr6jV//u+sIjlV81RUZNur5hwRAutiUinaumHjgNe9b++BN9aftkBH/hfP27kNRGRDlVYnN8Db8GQ1SN2ztsp6DzdUH4ow56eWlJUBswBtiahqL20hBVX3MacqmpNbZduKx/QNGoR6VT6y5CIdHW7A9kZmVY3cFj2DkGH6ab6Ak9NLSmqxJvm/o2i9sobrLz8Vm6PVqmoSbelKY8i0qmSUdJcg68NnxcR6UiTgE07T+g9IjM7lBt0mG6sD15Rqwb+hVfUcuIvvvo/Vpb8iznRKiqCCijSgbR4iIh0qi0taQY84N+/Dcg1s3n+43nA7Vt4fBGRJhUW5+fiLY9dus3ovB2DzpMGegFPTC0pigG3AFuRUNQWvsWqS27mtsoom4IKKNJB9iEcac2lh0REkiIZI2nWzFddAFJEOtL2QAZQN3hErs5H6xy9gMenlhQZcBMNitqid1h9yc3MUVGTbqYXMDHoECKSPra0pDngB/79nwFR59xU//E04IQtPL6ISHPGArXDd+oxsEevjP5Bh0kjPYHHppYUZQE3AsOA+qmmi99l9d//yW0VlWwMKqBIB9B5aSLSaTrynLSmnhMR2WKFxfkZeH/ZXrvTuN4aRet8ecCjU0uK8mikqL2xlDUX38htFZWUBxVQJMl0XpqIdJq2lLSmClfDaY4iIp1hOF5RqBq6raY6BqQHMG9qSVFv4B/AEBKK2psfsPZvN3DbJhU16R4mEY5kBh1CRNJDa0rad/yvewONnTQbX/J623YcW0SkvUYD5Bdk5fXpn7l10GHSWC7w8NSSon54RW0oXnkD4O0PWXfh9dy6sYKyoAKKJEku3/5dR0SkQ7RUpAx40v8aBuLnfGT7+xrwv4TXMbP4tXMGJTusiAhAYXG+AXsBpbtO6rOjmWkkP1g5wINTS4oKgGvwRtTqi9q7H1F64fXctrGCDUEFFEmSXYIOICLpoaWS9kNgArAHUATsC6xj84fvycBPgKPxFgk5Fe/DehPeh3QW3sprIiLJNBDvZ8zG4Tv00FTH1JADPDC1pGgYcDUwGG86KgDvfUzpBddxW/km1gcVUCQJdg46gIikh2ZLmnNukX972zn3hnPuZbxz0zKBkHPuFufc3c65+5xzdzrnbnfOlTvnejvnFuN9QOc19x4iIu2wM2BZOZbRf0j2dkGHkXrZwH+mlhQNxytqg0j4DHj/U9arqEkXp5E0EekU7TlvrAcQwytqLSkFLm3He4iINGcSUL7t7j2HZmSaLjCbWrKAe6eWFI0CrgIKSChqH3zGhvOv4dayjZQGlE9kS6ikiUinaHNJ80fKspxzH7di29XOufPbF01E5NsKi/N7AjsB64eOyh0WdB5pVBYwd2pJ0Q7AlXjTU3vGX/zoC8rOv4bbyjayLqiAIu2k6Y4i0ik6ZAXGhMVDRESSbXu8xYpiA4Zlq6Slrkzg7qklRbvgFbUBJBS1j7+k7NzZ3LahnLVBBRRphz6EI/q5IyIdLuklzcwOBS5K9nFFRHzb4U25ps+ArKEBZ5HmZQJ3TS0p2h24HK+o9Yq/+NlXlJ87m9vWl7EmqIAi7aDRNBHpcG0qaWZ2tJm1tLT+6cAfzOzo9scSEWnSzkB5bl4oK69XRkHQYaRFGcAdU0uKioASoB8JRe3z5Ww8dzZzSstYHVRAkTbSeWki0uFaXdLMbCpwFzDXzBrdz8yOBw4GFgL3JiWhiIivsDg/AxgFbBy5a94QC+n6aF1ECLhtaknRRDYXtd7xF79YwcZzrmTOug18HVRAkTbQSJqIdLi2jKQ9C/wLKAZmN3zRzAqBG4DVwI+ccy4pCUVENivAG5mpG7pNrqY6di0h4JapJUWTgMuAviQUtWWr2PTnK5mzbj2rggoo0koaSRORDtfqkuacq3DOnQJcAPzWzP4Qf83MJuGVuFrgUOfcsqQnFRGBYXiLhjBgaI5O3u96QsDNU0uK9sUran38GwDLv6Zi5hXMWauiJqlNJU1EOlx7luC/AG+6yqVmdoyZzQCeA8qAff2LWIuIdIRtqF80JFMlrWsy4MapJUUH4F1HszcJRW3lGipnXs6cNaWsDCqgSAuGEY70ankzEZH2a9fqjs65s4FFwJ3AxcAtwB7OubeSmE1EpKGd2LxoyMCgw0i7GfCPqSVFBwGX4C3N3zf+4qq1XlFbXcqKoAKKtGBw0AFEpHtrc0kzs/3M7ClgPPAUMMk591vn3MakpxMR8fmLhmwDbBy1W08tGtL1GXDd1JKiQ/FG1L5R1L5eR3RGCbevXsfyoAKKNEN/JBKRDpXZ1AtmNgpvoZBqvA/TfLzrE/XzN/kUiAIzrfHflRzwJXCec64saYlFJF0Nwl80ZMioXE117D6unlpSlPHIWYtnATPwPm/WA6wpJXp2CbdfciY/GTSArQJNKfJNKmki0qGaG0nrCewNFOItNzsIKAe+8G8ZwNhmbvsBp+GdvyYisqXqi9mAodla2bF7uXJqSdERwCwgF++PggCsXU/V2SXcsWoNWpBKUolKmoh0qCZLmnPuHedcjnNuqHNupHNumzbeCoAFwBGd9t2ISHc2CqgD6N0vc1CwUaQDlEwtKfoRXlHLYfOsDdZt8IrayjV8GVg6kW9SSRORDtWmc9LMbGcze93MZpvZFDPLbWGXlcBzZpbd/ogiIoC37HU5QHZuqE8L20rXdMnUkqJj8IpaFglFrbSM6j9exp0rVvNFYOlENlNJE5EO1eQ5aU3IAMbhTWc8DdhkZncDVzvn3mlk+5OdcxVbmFFE0py/aMhIYEVGpoWyciwv6EzSYS5ucI5af2AdwIZyr6hdehbHDRvEyEBTSrfinKOmrq66uqYmWl1THa2qqYlWVVdHK6uro9HqqmhlVVV0bdn6HpOLJtzaM7fH10Bjv/OIiCRNW0ta3CFAL7ypjCcAJ5rZXcAM51z9RUhV0EQkSfLxRv5j/Ydm97EmViuSbuPCBkVtALAWoGwjNX+8jLsuPYtjtxrMqCBDSupwzlFTW1tVXVsTra6piVbV1ESjCQWrsqoqWlFVGd1YGY1urKyIlldsipZt2lRVurE8uq5sQ3Rt2YZobV2da+Ftht/06EP3zXvp+TWd8k2JSFprb0mrcM79F3jIzP4E/AU4CZhiZj91zj2etIQiIt4Fjx1Av4Ks3gFnkc7x16aKWvkmr6j9/Q+cMHIYwwNNKUkRc87V1NZWfWMkq6a6qrK6OhqtqopWVEWjFdFodFO0Mlpe4ZWsDZs2RkvLy6NryzZE15WXVcVisZZKVmuF8H4/anjLATSKLyKdoq0lLYT3i1JG/Ann3HLg12Y2F5gLzDOzM51zs5MXU0TSXG+8pdnp3T9TJS19/KVBURsIrAHYWEHtjMt55IoZTB42iJ0DTSnEYjFXU+eVLL9gbZ4uGC9ZVdHopsrKaHllRbR8U4VfssrqS5ZzyepYZNB4yYrfAGL+1/ibxkfnQ3iXHqoANgGlwEa882HXA/WzhUREOlJbS1ou3g+yXg1fcM4tMLNxwPPAFWZW4Zy7KQkZRUTqS1qvfJW0NPPnhKI2k4SiVlFJ3VmX8s+7SzgYbxq+tFMsFotVJ04XrK6ORmu8glVZXRWtiManC1ZGN1ZURMvqpwuWRddu2BAt3VjemSXLJdzizL+FgCo2l6y1bC5Z8VsF3nVeG7tVzXvp+dpkfSMiIu3V1pJWDjyM9wPvW5xzy8xsCvAY8MAWZhMRieuH/wtZXu8MlbT0M8Mvan+nkRE1YDpwf4D5AlcXi8VqEs7H8kayaqoq68/JikY3RTePZJVt2lg/XXBN2froho0bq5MYp7UjWY2VLMMrWZv828aEWzlQBlTSfMmqS+L3IiISiDaVNOfcUlq47plz7j0zG+uc27BFyURENivAm4JEj14qaWnqrAZTHwvwr5vHzq6KpfYDoMuOgNTFYnU1NTVVVX7RilbHpwvGF7345jlZZRWbohs2lkfXlZdH15atj5Zt2lSTxDjNFawMGh/JAm8Uy/DKUmLJin9VyRIRaaX2LhzSLBU0EUmygXh/XSc3TyUtjf1+aklRyB9RmwkMAT4EYGeXzJGgNqurq6urrq31R7Kq/dUFq6LR6ur6krWpsjK6MV6yNm2Mbti4MVq6sbxqbdn6aHlFRbJKltH8SFZTJStxJKthyWo4XbClkhVDRES2SIeUNBGRJBuAX9Jy8kIqaentdH9E7ULgT8k6aF1dXW1FVXRj/XTB6ur4Eu5V9SUrGo1uqqyIlldURDdUeCVrXVlZdE3Z+mhFNJqsUbxklKwK/1aOV7TK8YpWmX+/vlChkiUikpKaLWlmlgEMxftBXkt8aknrVDjnuuzUExFJDYXF+YZ3QeOvAbJzVdKEU6eWFGU8/uc3zqurjiXlmnnH/e0vyVqR2Gh+umB8leSmSpbDG6mKl6zEc7IalqzGbtUqWSIiXV9LI2nbA++289gxM7vfOffjdu4vIgLetYmygLqsHMvIyg71CDqQpIRfH3bxmAzgV0k+bmtKVsNFL+L7xV+rxBvB2kDj0wVbKllJWyZRRES6ptZMdzTggoT75wHX4a+shXfy9m8StonbG/iRmV3lnHs1CVlFJD3VX8h6wJBsjaJJolOA0E2Lx51yctGithabGF6p2ppvXiMrxrevkRUvWfH7KlkiItKhWlPSnHOuvoCZ2XnANc65D/zHOwG/TtzGf34S8F1gX0AlTUTaq76k5eRlZAecRVLPSUDGTYvHnXRy0aK2TPNbAfwfXilLLFk1KlkiIhK0jlw4ZBkwFXihA99DRLq/+gtZZ2RaKOAskpp+jlfUft7aouYXsc86NJWIiEg7ddgvPM65L51zjznnyjrqPUQkLfTC/1mVmWUZAWeR1HUCMOemxeP0/4iIiHR57S1pmgoiIp0lK34nI1MlTZp1PHCHipqIiHR1rVo4xMyeb/Dc3WZW6d/v4W/UcJtMIBf4oXPuky2LKSJprP4XbpU0aYVj8KY+Hndy0SJdBkZERLqk1p6T1nDJ69xG7iduE78YZw86cEqliKSFTPzR+5DOSZPWOQpv1cdjVNSkI8wunZ0N9AP6nN7v9A+DziMi3U9rV3ecEH9gZjHgyITVHXcG3kncRkQkibLwVuCjz4ZeriCy1RcODMOBM2cADmcufjlg8+Zj1z/G4W8V38bbz3lbYw7n6u/VXxrZmbcvEIrv7x8XzJnDzPvqz/+2+J+kHH5CnBHC1R81/h6YmTnnwFzImcXThhJmkpszZ5gzMOL7eUfF+z78I2HfSGdmFt82VP/dGCEX/xfB/GNbKCnXgU5VP8QbUTv65KJFNUGHkdQyu3R2Dl7Jym/h1reJ53MTjpV5er/T6zozv4h0f8lY3VHnp4lIR8rC/zmzdemQzANWjBsRcJ5uxzmHq/9Z7pwD77F3t/41l3DXJWyLa/B481OuvjDX38e5bx4j4b291xocy1H/nPfA1cfZ/EqNq8vKCWVW9MnOXWdGzNvORj026cgzgVlb/I8kKWV26ewetFywmitcOUmM0wvvwuUiIknTkUvwi4hsOUeWOcsEsuvqYjonrQOYmTda5z/a/EJAgdovn1qGxR88Pf6I8Nf5u5wza0nN9TPHZumX6BQyu3R2T1oerWqubKXSNRN7o5ImIknW3pKm0TMR6RT5Zb1HZNRmTsIcFXmxnkHnka7hw2G7LPp46M774p0XfTJQEnCkbmV26exetG16YMNtsr591C6rd9ABRKT7ae3qjvMbPHeHmVX49/P8je4F3gdeAp53zlUgIrKF8irzlgFhYMXgnL7b4S2zLtKkLyyn9pmxU7c3q19o5rRZS2qumjk2S4uI+GaXzu5N+8/H6otm4iTqFXQAEel+WvohGwUieNMKavBO3p8P/gn33gqOtcAbwHeAH+D91bLSzB4CrnfOvdgx0UUkTdSP3FfW1GgBCGlWtcM9+J2jNmVnZPZNeHoEcCQwN6BYSTW7dLbhjd6093ysviRc2kK2WG7Lm4iItE2zJc059zkwsbUHM7McYG9gOnAC8GMzu9w5d/YWpRSRdFZf0jZVVVUFGURS3+3b7b82e/DwgY289AdSpKT5JStemtp6PlY+0Add3iaVaGVHEUm6pE5XcM5V4Y20zTezc4G/Ag8l8z1EJO3U4S9hUaaSJs14ss/IsprRezVW0AAmzlpSs9fMsVkvb+n7zC6dHaLxctXawtUblazuRNNoRSTpOmxOuXOuDO8vlyIiW6ISf2rWhmhUJU0a9UFGz+qP9v5Rbgtz+P4ANFvSZpfOHoM3db+5wtWbrrj2pXQUlTQRSTqd+CsiqW4T/qjD+mi0OuAskoI2Ye7JvY+rzMrK7tvCptNnLakZNXNs1mfNbLMG+BP6fJTW03RHEUm6Vk23MLOhZrZLE6/dYGa5DZ4bb2bXJSOgiKS9Kvzz0qpqa+vqYjH9QiTfcPsuh6zJyh/YUkEDb0T2dw2fHH3I6JzRh4wePPqQ0SNvOuamnhXrK55JfkrpxjSSJiJJ19o58X8GFjd80i9upwD/TnjuD8CLwC/MbJ9khBSRtFZf0gBq6uo05VHqPTBgp/XsMLagDbv8YtaSmj4NnjsKuAz4C/DHF/75glYRlbZQSRORpGttSVuHd15IQyfj/fJ0i5n1MbPH8D7oXgXGavl9EUmCahJLWiymkiYA/C+rb3TFnoe39RpVfYBfNHjuLbxLzCwDln2x5IslZavKvkhGRkkLGt0XkaRrbUmrpsFfivxRtN/gXbj6EbwStxXwN+AAYJSZtWb6iYhIc75RyipqajYGFURSRykZsfC+x9eEMjLbc+7Y72YtqUlcY+RtvD9G9ow/8UH4g1e3NKOkDY2kiUjStWsJYDMbCDwMVOD/RdI5VwN8xzl3Pt6qWPcCn5nZMUnKKiLp6RslbWNVVVlQQSQ11DnHXYWHr8vq1bd3Ow8xCjgi/uDtJ9+uBR4F6pfvf+PhN5ZGy6OlWxRU0oVKmogkXZtLmplNAF4HBgOHOec+ib/mXycNvOWJr8RbovjCJOQUkfRVRcJy5xuiUZW0NDd32Nh1GSN3bup6aK31+waPXwNqgCwAF3Pus9c/e20L30PSg6Y7ikjStaWk5ZrZ/XgfZJXAJOdco9NBnHNfOufOA67Hm/8vItJe5SSUtNLKyg0BZpGAvZJbULF+3CH5STjUXrOW1Hwn/uDtJ9/eBDwNDIk/F5kbWVJbXatzIKUl+pkkIknXlpLWCyjEuxhooXPu3YYbmNkRZvagmcWPq3NHRGRLRfFGODIA1lZUaCQtTa2w7NrIvsdioVC7puo3ouFoWhjvDwIhgMqyyurl7yz/1srGIgnKT+93ekXQIUSk+2n2g87MJiU8XOuc2945d5V//hlmdryZnerffxq4H9gN2LGjAotIenni/fcd3gWGcwBWlperpKWhGgf3TfjRhswePfOSeNgjZy2pGRF/8PaTb68GIsCg+HOL71/8mos519jOIsCqoAOISPfUZEnzFwd5ycxWAD8BMsysR8LrxwFzgN/5z88FjnPO7eicW9rBuUUkvazGL2nLNmxQSUtDd47ca03WkJEDknzYTOC0Bs89hf//GsDqj1dvWPPpmm/NHBHxqaSJSIdobiRtIHAr8BEwHMgHPjWzM8xsO+AW4E1gT+dcpXPuFufcPQ2O0dbr14iINOZrIBdg2YYNG2OxWCzgPNKJnu251cbKwuItXSikKSfPWlKT+Fn1CfAZ0C/+xFtPvPVKB723dH0qaSLSIZosac65pc65Xzjn9gUuxltu/zPgCuBuvFUbD3XOrYvvY2bXmVnEzF4xs1eB36ETakVky60CsgFizrny6up1LWwv3cRnGT2q39v3x1lm1vLG7dMXODH+4O0n33bAIyQsevXxSx9/Vf51+bKOCiBdmkqaiHSItpx8XeGc2xM4CdgVbwGRrRts0w9vaf7B/v338AqeiMiW2ADUj56tq6hYHWAW6SRRzM3b85hNGdk5OS1vvUVOn7WkJvHz8E3+v707j467Ou8//r4jjbwvsvEGGLMYA0YEzFoWYyBpYooT0gQSYkibNGmWNlRp8uspNDlwmqS/KP1laUhK0qZpQgwkjgmrQTLgRTbeJcuSv5Y1kjdZlm3J2ndpNHN/f3xnxCC02LI039Ho8zpHR7pf3Zl5ZOvM6Jl77/NAA9Bz/q10c6lW06Qvp7wOQESS01lXyLLW/i+wHLeXTI4xZmHM91Zaa+dbay+11l5hrb3RWvubYYxXRMamRqCneENVS0u1h7FInDxz+T21/plz0wefec4uBe6PDmKaW8+KXtv78t4DnS2dDXGIRUYXraSJyIgYYjWiqgAAIABJREFUUhlja+1W4D7cbSIvGWPGAxhjZhljrh7G+EREwK3u2LPfraKhQUlakls7/dLG8FW3jNQ5tL4M2Nw6HArbo7uP7opjPDI6KEkTkRFxpknaONwqWD2stVuAf8Mtuf/vkctfBoqMMbuMMV+KrQYpInIOGojplVZWU6MkLYkVp07pPHrbJ4ez1P6ZWJpVELwxOnBynBZgPe72fQDy1uTtCQVDXXGOSxKbkjQRGRFnmqRNI1JZrZfvAGXA3xljrsR95/FnwEzgl8BxY8w9wxGoiIxdkV5pFcAkgAPV1XWhcDjkbVQyEpqsL/z2HQ93pKT6/R48fO/VtE24bwz4ANrq2zrV3Fp6Oel1ACKSnM40SSsEepfXx1obBrKARyPVIN+y1n7dWnsZ7nbIAG5jUBGRc3WESJIWDIfDTR0dNR7HIyNg1TX31fmnzpjm0cM/mFUQ7CmI5eQ4VbivYT1n0wpeKlBza4lqB455HYSIJKczStKstf9rrf1CP9/7jbX2F31cz7bW3matVeNZERkOR4mcDwKoaWvTlscks2bW1fW+S6+J5zm03vzA13pdexPo2bpfVVrVUFteWxLXqCRRHchMz1TPRhEZEUMqHHImjDFXGGMeHKn7F5Exp5qYMvwVDQ0nPIxFhll+2oz26ptXTB185oj7UlZBcFLM+CBQDkyPXnByHJXjF4D9XgcgIsnrrJM0Y8yZ3uY+YNXZ3r+ISD+qiXnO2nfqlLYZJYlaUkNblz4c8qWkpHgdC26Pz89FB5Hm1q8Sk6SVbS6raD7dXBn/0CTBKEkTkRFzVkmaMeYHuHuwz0QH0HnWEYmI9K0Z9/nHD5B3/Pip7nA46G1Icq5C1vL89Z+o90+aMtnrWGJkZhUETcy4CLdXX8+2x7ItZTviHpUkGiVpIjJiznYlrR14T/lhY8z/McYU9TE31HuuiMhQRSo8BoCp4BYPqWlt1ZbHUe75C2+qTb1woZfn0PpyOfDR6MDJcYK8v7l1cWdrp85cj21K0kRkxJxtkhbk/atjU4CL+pmvClgiMpwcIhUeAY41NGjL4yi2ZcKc1ubrPzTD6zj60bsc/w7cNx9TAULBULg8r3xn3KOSRNGKW8xIRGREDKVwSO9KRsHIh4jISCsn5s2fwOnTFR7GIufguG9c9947V/qM8ZnBZ3virqyC4JLowMlxmlFza3nXgcz0TL0RLSIjZjiqO1q0YiYi8XE88tkHsLOiosKqZdWo02UtL978qabUcRMmDD7bU301t/YDBqC1trXj5IGTe+MdlCQEbXUUkRE1YiX4RUSGW3Yg0Im7xWgyQE1ra0djR8dpT4OSs7bq0mU1/tnzE3WbY6yHsgqC86IDJ8c5BewBZkevFbxUsMPqnYKxSEmaiIyo1P6+YYy5DFgDtPHuFsf5wDRjzOaYqX1dA5g7nIGKiETsA1YATQBH6usPLZkwYdbAN5FE8eaUi5q7rrkj0QqF9Cfa3PpbMddygOujg1Mlp+rryusCMy+eeWW8gxNP5XsdgIgkt4FW0vzAxbhFQeZHPqZFbjN/kGvzgdHwLqmIjD6HiHnu2lNZWephLHIWDqZM7Cq948E0r+M4S1/OKgjGbsssAypwX/sA2P/mfpXjH1u6ADU0F5ER1W+SZq0tsdbOsNZeZK29xFp7CfBjoD467u9a5Pq34/QziMjY8p6KjrmHD5cHQyrekOjarLFv3LayLcU/bpzXsZylmcBfRwd9NbcObAyUt9S2nPQgNvHGrsz0zDPtGSsiMiRneyatr333/e3F1x59ERl22YFAI24BkSkAXaFQ+Hhj42Fvo5LBPHPVh2v96bOnDz4zIX29V3PrQqCF9za31srK2JHrdQAikvxUOERERqMdxKxkFFdXa8tjAnt5xqIGFt0wWs6h9eUK4N7owMlxuujV3LrgpYL9XW1dzR7EJvGnJE1ERtygSZox5uvGmGUDTEkDRtv2FREZ3Q7EDjYdPlymAnuJaZ9/WkflrR+f7HUcw+AbvcbbiW1u3RUKl+eX74p7VBJv3cA2r4MQkeQ3YJJmjBkHPAFsMMbsA/68j2kTiNnyISISB8eADtw3iahoaGipa28/5W1I0lsDvvDGOx7u8qWk9ltJeBT5YFZB8APRgZPjNOH2Tetpbp2/Jj8/1B0KehCbxE9eZnpmq9dBiEjyG2wlLQX4B+B/cCtZ3QmkG2N+bYxZHJnzOJGzIb2k4laIFBEZVtmBQAi3BHZPFdmympqAdxFJX579wMfq/FOmT/U6jmHUu7n1BmKaWzefbm4/VXKqMO5RSTxpq6OIxMWASZq1ts1a+6y19svAAuA+YB3weaDQGPMda23IWtvZx83HRz5EREZCAZGVNID1Bw86HsYivfxhzrV1KRcvHs3n0PrymayCYM/KmZPjnMQtItJzNm3vy3t3aOttUlOSJiJxccaFQ6wr21q7ArgNt6FsaICb5AE/Ocf4RET6Uxb5bAD2nTpVU9vWpjLoCWDn+PPa6m66d7RWchzIOODve13LBiZGByf2n6itr6hXIZvkFALe8ToIERkbhlTd0Vq7A7gF+MUAc3Kttf8y1MBERAaSHQi0AKXEVHksOnlyn3cRCUA1/tCuO1Za4/Mla/Xgr2QVBGN3iZQClby3ubXK8Sen3ZnpmargKSJxMeQXUWtt0FpbPZzBiIicpVyg58xTdiDgWO0180zIWv5w0wMNqRMnT/I6lhE0C3gkOnBynDDwCjFvFpSsLznaWteqQjbJ50WvAxCRsSNZ3+kUkbHBAcJEnsuONTQ0n2xuPuppRGPYqoturfXPu2Sm13HEQe8CInuBVmLOYR985+COuEYk8fCC1wGIyNihJE1ERq3sQKAZt3BDT2JQUFmpLY8e2DRpXkvbdXePhQQNYHFWQfAj0UGkufXrwOzotT0v7nG62rtavAhORsSezPTMI14HISJjh5I0ERntNhPTq/H1kpLiUDg8UFEjGWblvvHBfXd8JtUY43Uo8dS7ufU23FXdVIDuzu7QsT3Hdsc9KhkpWkUTkbhSkiYio10J0E3kj+O69vbOI3V1B7wNaezosNhXbnmoOXXc+LHWcuXDWQXBq6MDJ8dpxH3DoGc1LW9N3u5Qd6jbi+Bk2ClJE5G4UpImIqNadiDQAWwnplfVm2Vlu7yLaGxZdfndtf5Z588YfGZS6n02bT2xza2rmturAlVqbj36FWWmZ5YNPk1EZPgoSRORZLCdmMbWm48cqahta1N1vRH2xrRLmroX35psDavPxsNZBcGeNwecHKcSt5hNz7/J3lfU3DoJaBVNROJOSZqIJIMyoIGYpsI7jh3TatoICqRO7jp8+wMTBp+Z1MYDX+117Q1ifg8r91XW1B+vPxjXqGS4rfE6ABEZe5Skiciolx0IhHD/OO5ZwXjRcfZ1dne3exdV8mrGF153+8PtKal+v9exJIC/yyoIjosZB4BTxPTvO/D2ATW3Hr32Z6ZnlngdhIiMPUrSRCRZ7MStrpcC0NrV1b2/qqrA25CS06rFy2v902ZO8zqOBDEHWBkdRJpbvwqkR68Vv1l8uK2+rdqD2OTc/dHrAERkbFKSJiJJITsQaAK24v7RDMDL+/fvtjoQNKz+dN5VDWbhdbMGnzmm9C4gsgdoI7a59TY1tx6FuoFfex2EiIxNStJEJJlsJKaASGlNTcOxhoZSD+NJKnv90ztO3fKxKV7HkYCuySoIfig6cHKcTiCbmIqje17YUxRsD7Z6EZwM2auZ6ZmVXgchImOTkjQRSSblwBFitpq9XlKy1btwkkctqaHNSx/p9qWkpHgdS4Lq3dz6ncjnFIBgRzB0bK+aW48yT3sdgIiMXUrSRCRpZAcCFnidmKINm48cqTjV3FzuXVSjX8hanl/y8Xr/5KmTvY4lgS3PKgheGR04OU4DbnPrnu23+Wvyd4dDYTW3Hh1KMtMz13sdhIiMXUrSRCTZFAEtQE95+OxAYLN34Yx+v7/gxrrU+YvGcj+0M2GAr/e69p7m1o0nG9uqSqv2xTswGZJfeB2AiIxtStJEJKlkBwJduNX1es4DrSstPXy6pUVnS4Zg24TZrU3X/3n64DMF+KusguDM6MDJcY4DxUDPtcJXC1WOP8FZa1uBZ7yOQ0TGNiVpIpKMtgKdQE//qjcCgU2eRTNKnTBp3flLVxrj8xmvYxklJgBf6XXtDaBnm2jF3orT9ZX1h+IalZwVY8xzmemZjV7HISJjm5I0EUk62YFAG+7ZtDkx1w5Wt7Qc9y6q0SVo4YWbP9WYOn7iRK9jGWX+PqsgmBYzPoDb3LqnKmbJ+hKV409sKhgiIp5TkiYiySoXCBJTkv+NkpJNnkUzyqy6+PYa/5yLZg4+U3qZBzwUHUSaW78GzOi5lu0cbGtoO+1BbDK4bZnpmYVeByEioiRNRJKOMWZ+diDQTK/VtJzS0kMnmpqOeBfZ6PDW5AubOz5wpwqFDF3v5tb5QDsx228Pbz+s1bTE9JTXAYiIgJI0EfGYMWaOMWZLr2sZxpi3BrjNvxpjNkU+Sowxjxtjvm+MWWeMMcDdkakbgW5iVtOeLyhYZ621I/GzJINDvgldgTs+neb+M8oQXZdVEIz+DuLkOB24za3fLcf/Qn5RsCPY5kVw0jdr7X5gjddxiIiAkjQR8ZAxJh23itqkmGsG+DFu6fI+WWuftNbeZa29C3CA3+FWc9wDLAGOAURW09YCc6O3zausrCo5fXrvsP8wSaDNGvv6bSvbUtLGjRt8tgyi92raO4Al0ty6q62r+3jh8by4RyX9Msb8a2Z6ZtjrOEREQEmaiHgrBHwaaIq59nncFbBBGWNuAo5baytxe1GlAnfinkeL2oC71aynb9qvd+/eEAyFus4t9OSz6ooP1fpnzJnudRxJYkVWQfDy6MDJcepxq472rKblrcnbFQ6FQ14EJ+9lw3Yf8ILXcYiIRClJExHPWGubrLU9pa6NMTOBR4AfnuFdZAI/i3ztAAuAMLDZGHMVQHYg0Ar8gZg/jo83Nrbsqqh459x/guTxavrCxvCVN+kc2vDpq7n128RsvW2obGitLqtWc+sEYHzmycz0TG2DFpGEoSRNRBJJFvC4tTY42ERjzHRgtrX2EIC19ifAKqANeBG4L2b6duAE0LNK9Ovdu7e3dnWpFxKwP3VqZ8Wtf6lS+8Pvc1kFwRkx4wrckvzvNrd+rVAFRDxmw3ZvZnrmS17HISISS0maiCSSZcAPjDGbgOuMMd8bYO79uI2CY00HmnEbWfc8v2UHAt3Ac0A67goHbcFg95ulpeuHL/TRqYmU8Po7Hu70pfr7PQMoQzYR+FJ04OQ4FrfiaE9z62N7jlU1nGhQxVEPGZ950usYRER6U5ImIgnDWrsopiDIXmvtt40x9xhjvtbH9I8Am6MDY8wioBDYBTzKe8+lARQDe4HZ0Quri4r2nWxqOjq8P8XosuqaFXX+qelTvY4jiX0tqyAYmwAfAE4Tk6iVbCjZHveoBAAbtvmZ6Zmveh2HiEhvStJExHORpKzPa9baDdban/fx/ZXW2j0x41JrbZG19oi19ipr7c7Y+dmBgAX+CIwnUmEP4L927ny1OxzuHrYfZhRZPfuaet8lV+sc2si6APhUdODkOCHc5tY9Wx73vbGvrL2xvcaD2MY84zNPeB2DiEhflKSJyJiRHQhUAm8B50evlZw+Xb/16NEzqiaZTPLSZrTV3PwX07yOY4zoXY4/D+gg2tzawuEdh3f2vpGMLBu2OzPTM3tvmRYRSQhK0kRkrHkNt7hIT2+2/961a3tta+tJ70KKr2r8oW13Phz2+VL0GhAfN2QVBO+MDpwcpx1YR8zW27w1eXuDncF2L4Ibq4zP/B+vYxAR6Y9eoEVkTIk0uP4tbkl+AxAKh+1v8vJeCVub9I1sQ9ay+oZPNPgnTpk8+GwZRr1X0zbj/v75ALpau7oriyrV3DpOwuHw85npmWrDISIJS0maiIxFe3C3nM2LXsirrKzaU1m5zbuQ4uO5+TfXpl5w2czBZ8ow+1hWQfCy6MDJceqAbai5ddyFw+E2n8/3T17HISIyECVpIjLmRIqIPAdY3EIiADy9fXtuU0dHrWeBjbDcifNaWpZ8cMbgM2UE+HCbr8d6i+i5NKC+or7l9KHT++Ma1Vhk+W5meuYJr8MQERmIkjQRGZOyA4Fa4HliVtPagsHuX+/e/adwOJx02x4rfOODRUsfSjHGZ7yOZQz7fFZBMLZYyzGglJhKj0Vri1SOfwSFgqEjvhTfj72OQ0RkMErSRGQs24Lbt6pny9nOioqTm48eTaom110W+9Itn25OHTdhgtexjHGTeX9z67XAlOi1o7uPnmo82Xg0/qGNDSbFfCEzPbPL6zhERAajJE1ExqzsQCAMPAP4idl29l87dmw73th40LPAhtnvLltW6591gbY5JoZHswqCqTHj/UANMc2tA5sCO+Ie1RgQ7Aiu/seZ/zjm2m2IyOikJE1ExrTsQOAk7vm0C4hUe7TAj7dsebk9GGz1MrbhkDN1QVMw43Y1rE4c84EHooO+mlsXvlYYaG9qr/MgtqQV6g41+cf7v+Z1HCIiZ0pJmogI5AK7iWlyfaKpqXV1YeFL1lrvojpHpSmTug7e/uD4wWdKnPUux78L6ATSALBwZOcRraYNIxuy38xMz6zxOg4RkTOlJE1ExryYbY+tQE9hh5zS0kN7TpwYlWX5WzE25/aVbSn+tDSvY5H3uTmrIHh7dBBpbv0mMc2t89fk7+3u7O7wIrhk09XeteWbc7/5P/F8TGPM/Hg+nogkHyVpIiJAdiDQBPwCSAd6zgz99J131le3tFR4FtgQ/e6q5TX+6bOmex2H9Kv3alou7muyD6CjuSNYua8yP+5RJZnuru6mtAlpDw40xxgzxxizJfL19caYt40xW40x3xzs/o0xGcaYtyJff98Ys84YY4C7h+UHEJExS0maiEhEdiAQAF4CLoxe6wqFwj/YtGl1a1dXk3eRnZ0XZ17RwOVLZnkdhwzo41kFwYujAyfHqQV2ELua9kL+zmRsBxFPHc0dX8hMz6zq7/vGmHTcVfRJkUs/Az4P3AF80hhzyQC3NcCPcQsPAcwC9gBLcNsriIgMmZI0EZH3eh0IEFOWv7KpqfW/du78Q3c43O1dWGem0D+t4+Sf3T958JnisRQGaW5dW17bXHOoRs2th6i5unn1txZ+64VBpoWATwPRN2FmWGsrrHsYtRaYOsBtPw/EVos0uKvwd+KujIqIDJmSNBGRGNmBQDfwKyBMzB9ouyoqTr6yf//LngV2BupJCecufSToS0lNHXy2JIAvZBUEY5OAo8AhoKddQtHram49FB3NHZW+FN/nBptnrW2y1jbGXNpqjPmaMWYlcDFQ1NftjDEzgUeAH8ZcdoAFuM8dm40xVw0xfBERJWkiIr1lBwI1wFO459N6VjbW7Nu3f1dFxRbPAhvEc9feX+efPG3K4DMlQUwBvhgd9NXc+sjOIyebTjVp69xZCHeHuztbOu//l4X/MpTCK18GSoCvAT+w/Zd3zQIet9YGoxestT8BVgFtwIvAfUN4fBERQEmaiEifIufTnsHtn9bzXPmTLVs2lNfXBzwLrB/Pz72uLmXBleqHNvr8Q1ZBMCVm7AB1vHtGikBuQKtpZ6Gpuul7Tyx+YkhFV6y1IdztzuD2T+zPMuAHxphNwHXGmO9Frk8HmnFbKuhvLBEZMj2BiIj0Lxe3NPpF0QsW+P7GjS/Wt7dXexZVL9vHz2pruHG5KjmOTguAv4wOnBynG3c17d3m1q8WBjqaO+o9iG3Uaa1r3fnk1U/+6znezfeAf46uohlj7jHGvKcRtrV2kbX2LmvtXcBea+23jTGLgELcvnePonNpInIOlKSJiPQjOxCwwGqgmJhG1w0dHV3f37jx2ZbOzgbPgos4adK685auxPh8ej4fvb7Ra7wTCBKpGmjD1h7ZdWRn3KMaZYIdwWYbtvcP5baRZCv69V9ba7fEjDdYa38+2G2ttaXW2iJr7RFr7VXWWv2ficiQ6UVdRGQA2YFAEPglbvW3ntWNYw0NzT/asmVVezDY6lVsQQtrbnqwMXXCpIlexSDD4tasguAt0YGT47ThVnqcG72W/8f8gu6u7k4vghsNwt3hUOPJxge/dfm3+i23LyIymihJExEZRKTR9U+BNGKKOhyorq57evv2Z7tCIU/+eH52wW01/rkLZg4+U0aB3qtpubgl3X0A7U3tXSecE2pu3Y9Tpaee+O71313ndRwiIsNFSZqIyBnIDgQqgJ/gVnzsWbnaffz4qf/dvfv5ePdQ2zDpgpb2a5epUEjy+GRWQbDn7KOT45zGPdvU09x6z4t7dtmwVXPrXk4Un3j19e++/n2v4xARGU5K0kREzlB2IFAC/Bz3D+ee0vybDh8+trqwcE3YxucP6KMpE7qKlz7kN8bE4+EkPlKAf+h17T3NrU8fOt1Yc6TmQFyjSnA1R2v25/4i91OR9gUiIklDSZqIyFnIDgT2AL/GLc3vj15/7cCB0teKi18ZoK/SsOjA2Ff/7KHWlLRx4wafLaPMF7MKgpNjxodxG1ynRy/sy96ncvwRzaebq/fn7P/wjmd36KyeiCQdJWkiImcpOxDYAvwemI+7AgLA7wsLi17ev/+lkVxRe+bye2r9M+elDz5TRqFpwN9EB5HVoVeBqdFrh7Yeqmyubj7uQWwJpbOls600t3TFi4+/eMLrWERERoKSNBGRockBXsPtodbzXLq6qGjfC/v2vRAOh4c9UVs7/dLG8FW36BxacsvMKgjGvjbvAxqIOQc51ptbh4Kh7oNbD37hd3/7u91exyIiMlKUpImIDEGkh9qfgA24DYl7nk9fdJwDzxcWrg6Fw6Hherzi1CmdR2/7pErtJ79LgZ5eXzHNrXuS88JXCg90tnjfo88L1loO7zic9d8P/fcfvI5FRGQkKUkTERmi7EAgDDwLrAcuJuY5de2BA6Wr9uz5/XBUfWyyvvDbdzzckZLq9w8+W5JA73L8O4BuImcgw6GwPbJ7bDa3PrLzyHObnt70hNdxiIiMNCVpIiLnIDsQCOEmatm4iVrPGbWc0tJDv8nLey4YCnWdy2OsyviLOv/UGdPOKVAZTe7IKgjeGB04OU4r8DYx5fjz1+QXhILn9ns12pRtKXt9/U/X/40qOYrIWKAkTUTkHEVW1FbjFnlYAKRGv7f+4MGjv9ix43cdwWDrUO57zayr632XfUDn0Mae3qtpm3DfAPABtNW3dZ7Yf2JPvIPySmlu6eZNT29a6eQ4YyoxFZGxS0maiMgwiCRqfwJexC0m0pOobSsvr/y/Gzf+T0N7e83Z3Gd+2oz26ptXTB18piShB7MKghdGB06OUw3k897m1jtteGRbPiSC0s2lO3N/mfuQk+M0eR2LiEi8KEkTERkmkWIir+Cuql0EpEW/V1pT0/Ctdet+fbKp6eiZ3FctqaGtSx8O+VJSUgafLUkoFfhar2vriGluXV1W3VBbXlsS16ji7ODWg3m5v8h9wMlxTnodi4hIPClJExEZRpFE7Q3gt8D5xJROr21r6/jn7OxVgdOnCwe6j5C1PH/9J+r9k6ZMHmieJL0vZRUEJ8WMDwHlwPToBSfbSdpy/Id3HN678ecbP+nkOGO+L5yIjD1K0kREhll2IGCzA4ENwE+AGcT8Ud0VCoWffOutl7eVl2/q7/bPX3hTbeqFC3UOTdKBz0UHkYIZrxHz+1S2payi+XRzZfxDG1lHdh3Zt/6n6z/u5DjHvI5FRMQLStJEREZIdiBQCHwP97l2duz3ntq6Nfclx3mxd4n+LRPmtDZf/6EZcQxTElvv5tZFQCMxK7Rlm8uSajXtaN7R/W//5O2POzlOudexiIh4RUmaiMgIyg4EjgLfAeqAC2O/t7qoaN9/bNnyP82dnfUAlWZc996ln/EZ4zPxj1QS1OXAiujAyXGCuKtpPSute1/Ze6CztTMpimqUbCjJe+tHb33cyXEOex2LiIiXlKSJiIyw7ECgBvg+cIBeTa/zKiurvpnz5updNQ3Nf7r5U02p4ydO8ChMSVz9NbdOBQgFQ+HyvPJR3dw6HAqHd6/enbvlV1sednKcg17HIyLiNSVpIiJxkB0ItAJPARtwE7VxAGFM6pGU8bd8Y0/x+oqKwwU2HE76kupy1pZlFQSXRAdOjtOC+3s0N3ot7495+aO1uXV3V3fX5v/evG7vy3u/4uQ4pV7HIyKSCJSkiYjESXYgEARWAb8C5gDpLROn3dbpn9AaSkktfOfZ/3hn2x/+87dd7a1JsXVNhtU/9hpvwm1ubQBa61o7Tx44uTfeQZ2rzpbO1jd/+OZLZZvLHnVynKRuJyAicjaMTf4+mCIiCefeK664pNM/7lt1U2bf0uUf9xrG9KyCTJ45d8LdX3jsL9PPX3C5lzFKQgkCFz+2xH8ieiFjecajwGKgCmDOFXPSP/rkRx81xoyKM40tNS116/7futV1x+q+4+Q4p7yOR0QkkWglTUTEA9mBwJG2cZMfC/rTnsGYCwB/9HsttafaX/v3rz9fvOm110LBrk4Pw5TE4Qf+vte1dcD46KAqUFVfV14XiGtUQ1RbXnvilSdeebruWN3jStBERN5PSZqIiEd25G+rscb3Y+B54AJi+l8B5L38v3tynvqXpxtOHVMhBQH4clZBcGLMuAyoILa59brEb259fN/xQ68++eoP2urb/s3JcRq9jkdEJBFpu6OISAJYvGzFQtyVkinACSAc+/0b7//ckkV33PuRVH/aOC/ik4Tx1ceW+H8ZHWQsz7gR9/emp6fYZ37+mS9Nnjl5nhfBDSQcCocKXy3My/tj3lPAaifHCXkdk4hIotJKmohIAijOXXsQeAK3vPoC3GStR94rvy3I+enjTzec1KraGPf1rIJg7JmzQqAZ6GndULYl8Zpbtze112dnZWfn/THvSeD3StBERAamlTQRkQSyeNkKA2QEZ5B6AAAM5ElEQVQAXwQmA5X0WlW74f7PXXfFHfcu16ramLXisSX+16ODjOUZHwYeAo4BpKSl+B75xSNfT5uYNqW/O4inqtKqg2/+6M3tHU0dTzk5Tp7X8YiIjAZaSRMRSSDFuWttce7afcC3gK30saqW/8pv977xo3/6edWh4gJr1VdtDOpdjn87ECLa3LorFC7PL98V96h6CYfCocLXCre9+uSrr3Q0dTyhBE1E5MxpJU1EJEFFVtWuBv6WflbVLrr21nk3fPSzy6ecN+8iD0IU71z72BJ/UXSQsTxjJXA37u8Ik8+bPP5TP/7UN1L8Kf7+7mAkdTR3NGz8z41bjxcefwP4XaQBt4iInCGtpImIJKjIqpqDu6r2Du6q2nsqQB4r3H7ype/93W/2Zv9hTWdbc4MXcYoneq+mbcRdSTMALTUtHadKTnnS3Lr6YPWhF/7phbXHC4//GPilEjQRkbOnlTQRkVEgsqp2FfBZYB5wCuiIneMfPzH1lge+dOuC6267IyXVn+ZBmBI/XcCCx5b4e3qMZSzP+DqwCKgGmLd43oz7vn3fo/Hqbd3d1d1RtLYoL39N/k7gP50c50hcHlhEJAkpSRMRGUUWL1vhB24HPg2k4Zbrf0+lvOnzFky+5YEvfXD2JVdea3y++PyFLl747mNL/E9EBxnLM64AHiOmHP8nsj7xmZkLZi4a6UBOHz5dsuGpDfuaqpo2A7/V6pmIyLlRkiYiMgotXrZiCnAf8BGgE6gC3vOEPvvSxelL7lu5dNYlV17r86Voe3vyOQ1c9NgSfwdAxvIMA3wXmAQ0Alz5wSsvXvrFpX89UgF0tXU157+Qn+tkOzXAc8AGJ8cJD3Y7EREZmJI0EZFRbPGyFefjrqpdB9QD7zuXNvOihdOuv++R2+cszLjel5KSEu8YZUR96bEl/l9FBxnLM24GvkrMatrK/1z55UkzJs0dzge11nLCObFnw883HOho6qgAfuXkOIeH8zFERMYyJWkiIqNc5LzaYuARYC5ustbUe970eQsm3/DRz94+d9G1N6SkpnpS9U+GXfFjS/xXRwcZyzPSgB8BrUTOLN78mZuvvfZj1358uB6wvbG9Ztsz27Yc3n64AXgReNPJcbqG6/5FRERJmohI0li8bEUq7oraA7jJWgN9rKxNnXX+xOs/9le3nn/lkpvUEDspLH9siX9ddJCxPGM58Clim1v/8pF/TJuQNvlcHiQcDoeP7jy6I/e/cg93d3aX4J49qzynyEVEpE9K0kREksziZStSgA8AnwQuxF1Vq+s9b9ykqf5r/vyBaxZce+uNk9LPmxfnMOXc5QFPAasfW+LvWcnKWJ4xFXc1rQroBrj7a3cvXXj7wnuG8iDWWk4fOu1s+8025/Th083A88AWJ8cJDXZbEREZGiVpIiJJavGyFT7cZtifAC4BmoHavuYuuO72869ceu+N5y24IkNbIRNXOBwKnT5ScvRY0Y7n//SdrzzR37yM5RmfBZbiVv9kypwpEx784YPfSElNST2bx2uobDi86/e73inPLzdAPvCck+PUnMvPICIig1OSJiKS5GJ6rP0lsBC3x1Y1vUr3A0ycNnPcNR9+4NqLrrnlxglT02fFN1LpT3NtVUVlcX7R/g0vO631p6fitl/4anHu2mBf8zOWZ5wP/BvulkcLcN+371tx/tXn33Amj9dS23Jy78t7Nxx4+0An0A78Btjj5Dj6o0FEJA6UpImIjBGRZO1S4E7cXms+3G2Qffa0uuzmey669MZl15y34PKr/OMmTIpfpALQ2dpcf7KsqCiwJbuo6tD+RmAO4AfKgLXFuWuLBrp9xvKMbwCX4Zbq54JrLjjv3sfv/fuBmlt3NHXU7X9r/8Y9f9pTj8UHZAPrnByneZh+LBEROQNK0kRExqBIn7UbgXuBWbi91qqB9/W4Mj6fWXjLhxZcfN1ti89bsOgq//gJ51SAQvrX3dXZUVNeuv/Qro2Fh3ZvrACmRT7CwDvABqCiOHftoC/eGcszrgL+GTgavfbJf//kyhnzZ1zee26wPdhauqU0d+ezOytDwZAf2Ay8pq2NIiLeUJImIjKGRc6tLQTuBm4GDAOsrhmfz1x28z0XXbzk9sXnLVi0OG38RCVs56ijpbGutuJQ6fHi/LJDOzeUd3d1pAHn4a50HgXWA0XFuWvf11ZhIBnLM3zA94DxRFoyLP7w4ktv//ztn+157OaO+sM7Dm/f/YfdFV1tXeOBAuBPTo5zfFh+OBERGRIlaSIiAsDiZSumAzcBH8JdXbO4Pdf6TdguvfGu+RdefeNlMy689JLJ6bMuMD6fL34Rj07hUCjUdPrE0eojB8qO5G0uqzq0vw53G+OsyOd63BWzPKDqTFbN+pOxPOPPgC8T29z66ZVfsSEbCmwKbNv78t7KcCg8HTgIrAYO6tyZiIj3lKSJiMh7RM6uzQOuxT2/Npd3E7Z+zyaNnzwt7ZIb7rxo3qIPXJJ+wcWXTJw2Y64xvv4PQI0R1lo6WhprGk9VlFeWFJQd3LH+cGdrUxC3+MeMyOcuYCuwHThcnLv2fdtOhyJjecY43HL8LUSaW0+dO3Vi06kmP5AOnMItqb9PyZmISOJQkiYiIv2KSdiuwU3YzsdN2Bpxt9D1+yIyKX3W+EtvXHbxnIVXXzJ19gUXTpyaPtuXknpWJeBHo+6uzvaW2qrKhpPHjlcfKTl+rGjH8bbG2k7craRTgOm4/27tuGXt9wAlxblrO0cinozlGffhtmGowN1GORm3NP9LQIGT43SPxOOKiMjQKUkTEZEzEknY5uImbDfhVg60vJu0tTBA0uZLSTVzL7/mvNmXLp6bfv6CuVNnzZs7cfp5c/3jxk+MQ/gjoruro729ubGmpbaqqrbi0PETJQXHT5Xti+1Fl4q7WjY+Mj6Gu1pWglsAZFhWzAaSsTxjOvDDSCwHgLXAASfHGfHHFhGRoVGSJiIiQ7J42YoJwAJgEXADMB83SQsDDUDrmdxP+gWXTJm36ANzp8+7aNak6eelT5ianj5+8tTpaRMnT/f5UlJGKv4zFQ51d3e0Nte1N9bVttbX1DadPlHTcLK8tvrwgdqWuur2XtMnAtE+ZhYI4hbjyAfKinPXNsY3elfG8ozFQBtQrm2NIiKJT0maiIgMi8XLVkwGLsZtnH09bl+vMG6Vwi7clbZWBlhti2V8PjNt9oWTps2dP3XKzDlTJqXPmjph6vQpqeMmjE9NG5eW6k8bl+JPS0vxp41LSfWnpfjTxvlS/eNSUlLTjM9nrA1ba23Yhm0YGw5ba8PWhsM2HP0c6g52drQFO9rautpbW7vaWts6W5taO1oaW9ubGtraGmtbm2urWhtOlrfYcLivmFNxE7LJkZ/JAFVAMRAAjuMW/nhf03AREZGBKEkTEZERsXjZikm42yPn4m6NXAhcyLsJTRg3aWvH7dM2fC9IxsDwvL4Z3K2KE3BXyXy4cRvcxLMUcHC3MR4vzl17RquHIiIiA1GSJiIicbN42Qo/MBs3cVsAXIpbmCSdd8+3Gd5dfeuMfA4CocjHcL1wpeKWvE+LfI5+Hd2yaSIfNbirYseAk5FxLdB4LuXxRURE+qMkTUREPLd42YpUYBpusjYt8jELN6GbBUzCXc0aH3OzoRa+iCaB7bgVKhtx2wtEP5oj12uAuuLctcEhPo6IiMiQKEkTEZFRI1JhMrriNS7m8zggBTdxG+gjhJuctRXnrlXpeRERSUhK0kRERERERBKIz+sARERERERE5F1K0kRERERERBKIkjQREREREZEEoiRNREREREQkgShJExERERERSSBK0kRERERERBKIkjQREREREZEEoiRNRERGLWPMfK9jEBERGW5K0kREJC6MMXOMMVsiX/+rMWZT5KPEGPN4P7fxG2NeM8ZsNcb8TeTa940x64wxBrg7jj+CiIhIXChJExGREWeMSQeeASYBWGuftNbeZa29C3CA3/Vz00eBfGvt7cADxpgpwCxgD7AEODbSsYuIiMSbkjQREYmHEPBpoCn2ojHmJuC4tbayn9vdBfwx8vVm4EbAAKnAnUDuSAQrIiLiJSVpIiIy4qy1Tdbaxj6+lQn8bICbTgKiCVwdMAd35W0BEAY2G2OuGs5YRUREvKYkTUREPGGMmQ7MttYeGmBaCzAh8vVkwGet/QmwCmgDXgTuG9FARURE4kxJmoiIeOV+4I1B5uQDd0S+vhY4Gvl6OtAMdKLXMhERSTKpXgcgIiJj1keAH0YHxph7gMXW2p/HzHkGeMMYsxRYDOw0xiwCCnGTtDeAz8UtYhERkTgw1lqvYxAREemXMeZ83NW0df2caxMREUkqStJEREREREQSiPbxi4iIiIiIJBAlaSIiIiIiIglESZqIiIiIiEgCUZImIiIiIiKSQJSkiYiIiIiIJJD/D4lG64PmKT+GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "——"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
